grudzień 11, 2015

0. Analiza wyników

1. Kod wyliczający wykorzystane biblioteki.

library(knitr)
library(dplyr)
library(ggplot2)
library(tidyr)
library(ggExtra)
library(corrgram)
library(RColorBrewer)
library(caret)
library(pROC)
opts_chunk$set(echo=TRUE, cache=TRUE)

Powyżej znajduje się lista wykorzystanych bibliotek.

Dodatkowo ustawione zostały zmienne globalne, które mają zgeneralizować wyświetlanie fragmentów kodu i utrzymywaniu poszczególnych fragmentów w pamięci.

2. Kod zapewniający powtarzalność wyników przy każdym uruchomieniu raportu na tych samych danych.

set.seed(23)

Powtarzalność badań zapeniła funkcja set.seed() z odpowiednio ustawionym ziarnem. ***

3. Kod pozwalający wczytać dane z pliku.

dane <- read.table(file = "all_summary.txt", 
                   header = FALSE,
                   sep = ";",
                   dec = ".",
                   fill = FALSE, #czy usupełniać kolumny
                   col.names = strsplit(readLines("all_summary.txt", n = 1), ";")[[1]], 
                   skip = 1,
                   na.strings = c('NAN'),
                   blank.lines.skip = TRUE, #Opóść linie puste
                   stringsAsFactors = TRUE
                   )

Do wczytania danych wykorzystana została funkcja read.table(). By dokonać dokładnego odwzorowania danych, znajdujących się w pliku tekstowym, do obiektu data.frame, ustawione zostały odpowiednie argumenty funkcji. ***

4. Kod usuwający z danych wiersze posiadające wartość zmiennej res_name równą: “DA”,“DC”,“DT”, “DU”, “DG”, “DI”,“UNK”, “UNX”, “UNL”, “PR”, “PD”, “Y1”, “EU”, “N”, “15P”, “UQ”, “PX4” lub “NAN”.

dane <- dane %>% 
  filter(!is.na(res_name), 
         !is.nan(res_name),
         !res_name %in% c('DA','DC','DT','DU','DG','DI','UNK','UNX','UNL','PR','PD','Y1','EU','N','15P','UQ','PX4','NAN'))

Jako, że zmienna res_name w późniejszych badaniach posłużyła jakozmienna klasyfikująca dany ligard, musiała ona być odpowiednio identyfikowana przez określoną wartość. Mając to na uwadze, usunięte zostały wiersze, których wartość zmiennej res_name była niedostępna, czyli równa NA. ***

5. Kod pozostawiający tylko unikatowe pary wartości (pdb_code, res_name).

dane <- dane %>% 
  distinct(pdb_code, res_name)

6. Krótkie podsumowanie wartości w każdej kolumnie.

Tabela przedstawiająca dane po wstępnym czyszczeniu

kable(summary(dane))
title pdb_code res_name res_id chain_id local_BAa local_NPa local_Ra local_RGa local_SRGa local_CCSa local_CCPa local_ZOa local_ZDa local_ZD_minus_a local_ZD_plus_a local_res_atom_count local_res_atom_non_h_count local_res_atom_non_h_occupancy_sum local_res_atom_non_h_electron_sum local_res_atom_non_h_electron_occupancy_sum local_res_atom_C_count local_res_atom_N_count local_res_atom_O_count local_res_atom_S_count dict_atom_non_h_count dict_atom_non_h_electron_sum dict_atom_C_count dict_atom_N_count dict_atom_O_count dict_atom_S_count part_00_blob_electron_sum part_00_blob_volume_sum part_00_blob_parts part_00_shape_O3 part_00_shape_O4 part_00_shape_O5 part_00_shape_FL part_00_shape_O3_norm part_00_shape_O4_norm part_00_shape_O5_norm part_00_shape_FL_norm part_00_shape_I1 part_00_shape_I2 part_00_shape_I3 part_00_shape_I4 part_00_shape_I5 part_00_shape_I6 part_00_shape_I1_norm part_00_shape_I2_norm part_00_shape_I3_norm part_00_shape_I4_norm part_00_shape_I5_norm part_00_shape_I6_norm part_00_shape_I1_scaled part_00_shape_I2_scaled part_00_shape_I3_scaled part_00_shape_I4_scaled part_00_shape_I5_scaled part_00_shape_I6_scaled part_00_shape_M000 part_00_shape_E3_E1 part_00_shape_E2_E1 part_00_shape_E3_E2 part_00_shape_sqrt_E1 part_00_shape_sqrt_E2 part_00_shape_sqrt_E3 part_00_density_O3 part_00_density_O4 part_00_density_O5 part_00_density_FL part_00_density_O3_norm part_00_density_O4_norm part_00_density_O5_norm part_00_density_FL_norm part_00_density_I1 part_00_density_I2 part_00_density_I3 part_00_density_I4 part_00_density_I5 part_00_density_I6 part_00_density_I1_norm part_00_density_I2_norm part_00_density_I3_norm part_00_density_I4_norm part_00_density_I5_norm part_00_density_I6_norm part_00_density_I1_scaled part_00_density_I2_scaled part_00_density_I3_scaled part_00_density_I4_scaled part_00_density_I5_scaled part_00_density_I6_scaled part_00_density_M000 part_00_density_E3_E1 part_00_density_E2_E1 part_00_density_E3_E2 part_00_density_sqrt_E1 part_00_density_sqrt_E2 part_00_density_sqrt_E3 part_01_blob_electron_sum part_01_blob_volume_sum part_01_blob_parts part_01_shape_O3 part_01_shape_O4 part_01_shape_O5 part_01_shape_FL part_01_shape_O3_norm part_01_shape_O4_norm part_01_shape_O5_norm part_01_shape_FL_norm part_01_shape_I1 part_01_shape_I2 part_01_shape_I3 part_01_shape_I4 part_01_shape_I5 part_01_shape_I6 part_01_shape_I1_norm part_01_shape_I2_norm part_01_shape_I3_norm part_01_shape_I4_norm part_01_shape_I5_norm part_01_shape_I6_norm part_01_shape_I1_scaled part_01_shape_I2_scaled part_01_shape_I3_scaled part_01_shape_I4_scaled part_01_shape_I5_scaled part_01_shape_I6_scaled part_01_shape_M000 part_01_shape_E3_E1 part_01_shape_E2_E1 part_01_shape_E3_E2 part_01_shape_sqrt_E1 part_01_shape_sqrt_E2 part_01_shape_sqrt_E3 part_01_density_O3 part_01_density_O4 part_01_density_O5 part_01_density_FL part_01_density_O3_norm part_01_density_O4_norm part_01_density_O5_norm part_01_density_FL_norm part_01_density_I1 part_01_density_I2 part_01_density_I3 part_01_density_I4 part_01_density_I5 part_01_density_I6 part_01_density_I1_norm part_01_density_I2_norm part_01_density_I3_norm part_01_density_I4_norm part_01_density_I5_norm part_01_density_I6_norm part_01_density_I1_scaled part_01_density_I2_scaled part_01_density_I3_scaled part_01_density_I4_scaled part_01_density_I5_scaled part_01_density_I6_scaled part_01_density_M000 part_01_density_E3_E1 part_01_density_E2_E1 part_01_density_E3_E2 part_01_density_sqrt_E1 part_01_density_sqrt_E2 part_01_density_sqrt_E3 part_02_blob_electron_sum part_02_blob_volume_sum part_02_blob_parts part_02_shape_O3 part_02_shape_O4 part_02_shape_O5 part_02_shape_FL part_02_shape_O3_norm part_02_shape_O4_norm part_02_shape_O5_norm part_02_shape_FL_norm part_02_shape_I1 part_02_shape_I2 part_02_shape_I3 part_02_shape_I4 part_02_shape_I5 part_02_shape_I6 part_02_shape_I1_norm part_02_shape_I2_norm part_02_shape_I3_norm part_02_shape_I4_norm part_02_shape_I5_norm part_02_shape_I6_norm part_02_shape_I1_scaled part_02_shape_I2_scaled part_02_shape_I3_scaled part_02_shape_I4_scaled part_02_shape_I5_scaled part_02_shape_I6_scaled part_02_shape_M000 part_02_shape_E3_E1 part_02_shape_E2_E1 part_02_shape_E3_E2 part_02_shape_sqrt_E1 part_02_shape_sqrt_E2 part_02_shape_sqrt_E3 part_02_density_O3 part_02_density_O4 part_02_density_O5 part_02_density_FL part_02_density_O3_norm part_02_density_O4_norm part_02_density_O5_norm part_02_density_FL_norm part_02_density_I1 part_02_density_I2 part_02_density_I3 part_02_density_I4 part_02_density_I5 part_02_density_I6 part_02_density_I1_norm part_02_density_I2_norm part_02_density_I3_norm part_02_density_I4_norm part_02_density_I5_norm part_02_density_I6_norm part_02_density_I1_scaled part_02_density_I2_scaled part_02_density_I3_scaled part_02_density_I4_scaled part_02_density_I5_scaled part_02_density_I6_scaled part_02_density_M000 part_02_density_E3_E1 part_02_density_E2_E1 part_02_density_E3_E2 part_02_density_sqrt_E1 part_02_density_sqrt_E2 part_02_density_sqrt_E3 part_03_blob_electron_sum part_03_blob_volume_sum part_03_blob_parts part_03_shape_O3 part_03_shape_O4 part_03_shape_O5 part_03_shape_FL part_03_shape_O3_norm part_03_shape_O4_norm part_03_shape_O5_norm part_03_shape_FL_norm part_03_shape_I1 part_03_shape_I2 part_03_shape_I3 part_03_shape_I4 part_03_shape_I5 part_03_shape_I6 part_03_shape_I1_norm part_03_shape_I2_norm part_03_shape_I3_norm part_03_shape_I4_norm part_03_shape_I5_norm part_03_shape_I6_norm part_03_shape_I1_scaled part_03_shape_I2_scaled part_03_shape_I3_scaled part_03_shape_I4_scaled part_03_shape_I5_scaled part_03_shape_I6_scaled part_03_shape_M000 part_03_shape_E3_E1 part_03_shape_E2_E1 part_03_shape_E3_E2 part_03_shape_sqrt_E1 part_03_shape_sqrt_E2 part_03_shape_sqrt_E3 part_03_density_O3 part_03_density_O4 part_03_density_O5 part_03_density_FL part_03_density_O3_norm part_03_density_O4_norm part_03_density_O5_norm part_03_density_FL_norm part_03_density_I1 part_03_density_I2 part_03_density_I3 part_03_density_I4 part_03_density_I5 part_03_density_I6 part_03_density_I1_norm part_03_density_I2_norm part_03_density_I3_norm part_03_density_I4_norm part_03_density_I5_norm part_03_density_I6_norm part_03_density_I1_scaled part_03_density_I2_scaled part_03_density_I3_scaled part_03_density_I4_scaled part_03_density_I5_scaled part_03_density_I6_scaled part_03_density_M000 part_03_density_E3_E1 part_03_density_E2_E1 part_03_density_E3_E2 part_03_density_sqrt_E1 part_03_density_sqrt_E2 part_03_density_sqrt_E3 part_04_blob_electron_sum part_04_blob_volume_sum part_04_blob_parts part_04_shape_O3 part_04_shape_O4 part_04_shape_O5 part_04_shape_FL part_04_shape_O3_norm part_04_shape_O4_norm part_04_shape_O5_norm part_04_shape_FL_norm part_04_shape_I1 part_04_shape_I2 part_04_shape_I3 part_04_shape_I4 part_04_shape_I5 part_04_shape_I6 part_04_shape_I1_norm part_04_shape_I2_norm part_04_shape_I3_norm part_04_shape_I4_norm part_04_shape_I5_norm part_04_shape_I6_norm part_04_shape_I1_scaled part_04_shape_I2_scaled part_04_shape_I3_scaled part_04_shape_I4_scaled part_04_shape_I5_scaled part_04_shape_I6_scaled part_04_shape_M000 part_04_shape_E3_E1 part_04_shape_E2_E1 part_04_shape_E3_E2 part_04_shape_sqrt_E1 part_04_shape_sqrt_E2 part_04_shape_sqrt_E3 part_04_density_O3 part_04_density_O4 part_04_density_O5 part_04_density_FL part_04_density_O3_norm part_04_density_O4_norm part_04_density_O5_norm part_04_density_FL_norm part_04_density_I1 part_04_density_I2 part_04_density_I3 part_04_density_I4 part_04_density_I5 part_04_density_I6 part_04_density_I1_norm part_04_density_I2_norm part_04_density_I3_norm part_04_density_I4_norm part_04_density_I5_norm part_04_density_I6_norm part_04_density_I1_scaled part_04_density_I2_scaled part_04_density_I3_scaled part_04_density_I4_scaled part_04_density_I5_scaled part_04_density_I6_scaled part_04_density_M000 part_04_density_E3_E1 part_04_density_E2_E1 part_04_density_E3_E2 part_04_density_sqrt_E1 part_04_density_sqrt_E2 part_04_density_sqrt_E3 part_05_blob_electron_sum part_05_blob_volume_sum part_05_blob_parts part_05_shape_O3 part_05_shape_O4 part_05_shape_O5 part_05_shape_FL part_05_shape_O3_norm part_05_shape_O4_norm part_05_shape_O5_norm part_05_shape_FL_norm part_05_shape_I1 part_05_shape_I2 part_05_shape_I3 part_05_shape_I4 part_05_shape_I5 part_05_shape_I6 part_05_shape_I1_norm part_05_shape_I2_norm part_05_shape_I3_norm part_05_shape_I4_norm part_05_shape_I5_norm part_05_shape_I6_norm part_05_shape_I1_scaled part_05_shape_I2_scaled part_05_shape_I3_scaled part_05_shape_I4_scaled part_05_shape_I5_scaled part_05_shape_I6_scaled part_05_shape_M000 part_05_shape_E3_E1 part_05_shape_E2_E1 part_05_shape_E3_E2 part_05_shape_sqrt_E1 part_05_shape_sqrt_E2 part_05_shape_sqrt_E3 part_05_density_O3 part_05_density_O4 part_05_density_O5 part_05_density_FL part_05_density_O3_norm part_05_density_O4_norm part_05_density_O5_norm part_05_density_FL_norm part_05_density_I1 part_05_density_I2 part_05_density_I3 part_05_density_I4 part_05_density_I5 part_05_density_I6 part_05_density_I1_norm part_05_density_I2_norm part_05_density_I3_norm part_05_density_I4_norm part_05_density_I5_norm part_05_density_I6_norm part_05_density_I1_scaled part_05_density_I2_scaled part_05_density_I3_scaled part_05_density_I4_scaled part_05_density_I5_scaled part_05_density_I6_scaled part_05_density_M000 part_05_density_E3_E1 part_05_density_E2_E1 part_05_density_E3_E2 part_05_density_sqrt_E1 part_05_density_sqrt_E2 part_05_density_sqrt_E3 part_06_blob_electron_sum part_06_blob_volume_sum part_06_blob_parts part_06_shape_O3 part_06_shape_O4 part_06_shape_O5 part_06_shape_FL part_06_shape_O3_norm part_06_shape_O4_norm part_06_shape_O5_norm part_06_shape_FL_norm part_06_shape_I1 part_06_shape_I2 part_06_shape_I3 part_06_shape_I4 part_06_shape_I5 part_06_shape_I6 part_06_shape_I1_norm part_06_shape_I2_norm part_06_shape_I3_norm part_06_shape_I4_norm part_06_shape_I5_norm part_06_shape_I6_norm part_06_shape_I1_scaled part_06_shape_I2_scaled part_06_shape_I3_scaled part_06_shape_I4_scaled part_06_shape_I5_scaled part_06_shape_I6_scaled part_06_shape_M000 part_06_shape_E3_E1 part_06_shape_E2_E1 part_06_shape_E3_E2 part_06_shape_sqrt_E1 part_06_shape_sqrt_E2 part_06_shape_sqrt_E3 part_06_density_O3 part_06_density_O4 part_06_density_O5 part_06_density_FL part_06_density_O3_norm part_06_density_O4_norm part_06_density_O5_norm part_06_density_FL_norm part_06_density_I1 part_06_density_I2 part_06_density_I3 part_06_density_I4 part_06_density_I5 part_06_density_I6 part_06_density_I1_norm part_06_density_I2_norm part_06_density_I3_norm part_06_density_I4_norm part_06_density_I5_norm part_06_density_I6_norm part_06_density_I1_scaled part_06_density_I2_scaled part_06_density_I3_scaled part_06_density_I4_scaled part_06_density_I5_scaled part_06_density_I6_scaled part_06_density_M000 part_06_density_E3_E1 part_06_density_E2_E1 part_06_density_E3_E2 part_06_density_sqrt_E1 part_06_density_sqrt_E2 part_06_density_sqrt_E3 part_07_blob_electron_sum part_07_blob_volume_sum part_07_blob_parts part_07_shape_O3 part_07_shape_O4 part_07_shape_O5 part_07_shape_FL part_07_shape_O3_norm part_07_shape_O4_norm part_07_shape_O5_norm part_07_shape_FL_norm part_07_shape_I1 part_07_shape_I2 part_07_shape_I3 part_07_shape_I4 part_07_shape_I5 part_07_shape_I6 part_07_shape_I1_norm part_07_shape_I2_norm part_07_shape_I3_norm part_07_shape_I4_norm part_07_shape_I5_norm part_07_shape_I6_norm part_07_shape_I1_scaled part_07_shape_I2_scaled part_07_shape_I3_scaled part_07_shape_I4_scaled part_07_shape_I5_scaled part_07_shape_I6_scaled part_07_shape_M000 part_07_shape_E3_E1 part_07_shape_E2_E1 part_07_shape_E3_E2 part_07_shape_sqrt_E1 part_07_shape_sqrt_E2 part_07_shape_sqrt_E3 part_07_density_O3 part_07_density_O4 part_07_density_O5 part_07_density_FL part_07_density_O3_norm part_07_density_O4_norm part_07_density_O5_norm part_07_density_FL_norm part_07_density_I1 part_07_density_I2 part_07_density_I3 part_07_density_I4 part_07_density_I5 part_07_density_I6 part_07_density_I1_norm part_07_density_I2_norm part_07_density_I3_norm part_07_density_I4_norm part_07_density_I5_norm part_07_density_I6_norm part_07_density_I1_scaled part_07_density_I2_scaled part_07_density_I3_scaled part_07_density_I4_scaled part_07_density_I5_scaled part_07_density_I6_scaled part_07_density_M000 part_07_density_E3_E1 part_07_density_E2_E1 part_07_density_E3_E2 part_07_density_sqrt_E1 part_07_density_sqrt_E2 part_07_density_sqrt_E3 part_08_blob_electron_sum part_08_blob_volume_sum part_08_blob_parts part_08_shape_O3 part_08_shape_O4 part_08_shape_O5 part_08_shape_FL part_08_shape_O3_norm part_08_shape_O4_norm part_08_shape_O5_norm part_08_shape_FL_norm part_08_shape_I1 part_08_shape_I2 part_08_shape_I3 part_08_shape_I4 part_08_shape_I5 part_08_shape_I6 part_08_shape_I1_norm part_08_shape_I2_norm part_08_shape_I3_norm part_08_shape_I4_norm part_08_shape_I5_norm part_08_shape_I6_norm part_08_shape_I1_scaled part_08_shape_I2_scaled part_08_shape_I3_scaled part_08_shape_I4_scaled part_08_shape_I5_scaled part_08_shape_I6_scaled part_08_shape_M000 part_08_shape_E3_E1 part_08_shape_E2_E1 part_08_shape_E3_E2 part_08_shape_sqrt_E1 part_08_shape_sqrt_E2 part_08_shape_sqrt_E3 part_08_density_O3 part_08_density_O4 part_08_density_O5 part_08_density_FL part_08_density_O3_norm part_08_density_O4_norm part_08_density_O5_norm part_08_density_FL_norm part_08_density_I1 part_08_density_I2 part_08_density_I3 part_08_density_I4 part_08_density_I5 part_08_density_I6 part_08_density_I1_norm part_08_density_I2_norm part_08_density_I3_norm part_08_density_I4_norm part_08_density_I5_norm part_08_density_I6_norm part_08_density_I1_scaled part_08_density_I2_scaled part_08_density_I3_scaled part_08_density_I4_scaled part_08_density_I5_scaled part_08_density_I6_scaled part_08_density_M000 part_08_density_E3_E1 part_08_density_E2_E1 part_08_density_E3_E2 part_08_density_sqrt_E1 part_08_density_sqrt_E2 part_08_density_sqrt_E3 part_09_blob_electron_sum part_09_blob_volume_sum part_09_blob_parts part_09_shape_O3 part_09_shape_O4 part_09_shape_O5 part_09_shape_FL part_09_shape_O3_norm part_09_shape_O4_norm part_09_shape_O5_norm part_09_shape_FL_norm part_09_shape_I1 part_09_shape_I2 part_09_shape_I3 part_09_shape_I4 part_09_shape_I5 part_09_shape_I6 part_09_shape_I1_norm part_09_shape_I2_norm part_09_shape_I3_norm part_09_shape_I4_norm part_09_shape_I5_norm part_09_shape_I6_norm part_09_shape_I1_scaled part_09_shape_I2_scaled part_09_shape_I3_scaled part_09_shape_I4_scaled part_09_shape_I5_scaled part_09_shape_I6_scaled part_09_shape_M000 part_09_shape_E3_E1 part_09_shape_E2_E1 part_09_shape_E3_E2 part_09_shape_sqrt_E1 part_09_shape_sqrt_E2 part_09_shape_sqrt_E3 part_09_density_O3 part_09_density_O4 part_09_density_O5 part_09_density_FL part_09_density_O3_norm part_09_density_O4_norm part_09_density_O5_norm part_09_density_FL_norm part_09_density_I1 part_09_density_I2 part_09_density_I3 part_09_density_I4 part_09_density_I5 part_09_density_I6 part_09_density_I1_norm part_09_density_I2_norm part_09_density_I3_norm part_09_density_I4_norm part_09_density_I5_norm part_09_density_I6_norm part_09_density_I1_scaled part_09_density_I2_scaled part_09_density_I3_scaled part_09_density_I4_scaled part_09_density_I5_scaled part_09_density_I6_scaled part_09_density_M000 part_09_density_E3_E1 part_09_density_E2_E1 part_09_density_E3_E2 part_09_density_sqrt_E1 part_09_density_sqrt_E2 part_09_density_sqrt_E3 local_volume local_electrons local_mean local_std local_min local_max local_skewness local_parts fo_col fc_col weight_col grid_space solvent_radius solvent_opening_radius resolution_max_limit resolution TwoFoFc_mean TwoFoFc_std TwoFoFc_square_std TwoFoFc_min TwoFoFc_max Fo_mean Fo_std Fo_square_std Fo_min Fo_max FoFc_mean FoFc_std FoFc_square_std FoFc_min FoFc_max Fc_mean Fc_std Fc_square_std Fc_min Fc_max solvent_mask_count void_mask_count modeled_mask_count solvent_ratio TwoFoFc_bulk_mean TwoFoFc_bulk_std TwoFoFc_void_mean TwoFoFc_void_std TwoFoFc_modeled_mean TwoFoFc_modeled_std Fo_bulk_mean Fo_bulk_std Fo_void_mean Fo_void_std Fo_modeled_mean Fo_modeled_std FoFc_bulk_mean FoFc_bulk_std FoFc_void_mean FoFc_void_std FoFc_modeled_mean FoFc_modeled_std Fc_bulk_mean Fc_bulk_std Fc_void_mean Fc_void_std Fc_modeled_mean Fc_modeled_std TwoFoFc_void_fit_binormal_mean1 TwoFoFc_void_fit_binormal_std1 TwoFoFc_void_fit_binormal_mean2 TwoFoFc_void_fit_binormal_std2 TwoFoFc_void_fit_binormal_scale TwoFoFc_solvent_fit_normal_mean TwoFoFc_solvent_fit_normal_std part_step_FoFc_std_min part_step_FoFc_std_max part_step_FoFc_std_step
110l BME 901 A: 1 3ag4 : 16 SO4 :1183 301 : 430 A :8630 Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA Min. : 1.00 Min. : 1.00 Min. : 0.00 Min. : 3.0 Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. :0.0000 Min. : 1.00 Min. : 3.0 Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. : 0.000 Min. : 0.00 Min. :0.0000 Min. : 24253 Min. :1.952e+08 Min. :5.160e+11 Min. :3.330e+06 Min. :0.2312 Min. :0.0178 Min. :0.0005 Min. :0.0000 Min. :6.875e+05 Min. :1.246e+11 Min. :9.688e+10 Min. :1.335e+06 Min. :4.728e+03 Min. :5.618e+09 Min. : 0.0637 Min. : 0.0011 Min. : 0.0008 Min. :0.0000 Min. :0.0000 Min. : 0.0049 Min. :0.0001 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0e+00 Min. :0 Min. : 1023 Min. :0.0005 Min. :0.0142 Min. :0.0042 Min. : 2.930 Min. : 1.793 Min. : 0.498 Min. : 1527 Min. :7.096e+05 Min. :1.011e+08 Min. :9.595e+05 Min. :0.0593 Min. :0.0012 Min. :0.0000 Min. : 0.0000 Min. :6.548e+04 Min. :9.611e+08 Min. :1.275e+09 Min. :4.130e+05 Min. :2.366e+04 Min. :3.899e+07 Min. : 0.0048 Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0001 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 47 Min. :0.0007 Min. :0.0133 Min. :0.0052 Min. : 2.499 Min. : 1.769 Min. : 0.4972 Min. : 0.000 Min. : 0.00 Min. :0.0000 Min. : 24186 Min. :1.947e+08 Min. :5.217e+11 Min. :2.023e+06 Min. :0.2313 Min. :0.0178 Min. :0.0005 Min. :0.0000 Min. :6.807e+05 Min. :1.231e+11 Min. :9.354e+10 Min. :8.184e+05 Min. :3.360e+03 Min. :5.502e+09 Min. : 0.0637 Min. : 0.0011 Min. : 0.0008 Min. :0.0000 Min. :0.0000 Min. : 0.0049 Min. :0.0001 Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.0005 Min. :0.0127 Min. :0.0044 Min. : 2.898 Min. : 1.947 Min. : 0.4978 Min. : 1038 Min. :3.280e+05 Min. :3.182e+07 Min. :3.230e+05 Min. :0.0592 Min. :0.0012 Min. :0.0000 Min. : 0.0000 Min. :3.748e+04 Min. :3.151e+08 Min. :4.195e+08 Min. :1.414e+05 Min. :1.786e+04 Min. :1.512e+07 Min. : 0.0048 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0001 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 37.76 Min. :0.0007 Min. :0.0122 Min. :0.0054 Min. : 2.496 Min. : 1.877 Min. : 0.4971 Min. : 0.00 Min. : 0.000 Min. :0.0000 Min. : 24331 Min. :1.955e+08 Min. :5.180e+11 Min. :2.142e+06 Min. :0.2312 Min. :0.0178 Min. :0.0005 Min. : 0.0000 Min. :6.937e+05 Min. :1.254e+11 Min. :1.013e+11 Min. :8.662e+05 Min. :3.121e+03 Min. :5.751e+09 Min. : 0.0637 Min. : 0.0011 Min. : 0.0008 Min. : 0.0000 Min. : 0.0000 Min. : 0.0049 Min. :0.0001 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0 Min. : 1023 Min. :0.0006 Min. :0.0138 Min. :0.0047 Min. : 2.917 Min. : 1.914 Min. : 0.4957 Min. : 4269 Min. :5.654e+06 Min. :2.385e+09 Min. :1.443e+06 Min. :0.0588 Min. :0.0011 Min. :0.0000 Min. : 0.0000 Min. :1.324e+05 Min. :3.851e+09 Min. :4.897e+09 Min. :6.988e+05 Min. :1.294e+04 Min. :2.080e+08 Min. : 0.0047 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0001 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 177.5 Min. :0.0008 Min. :0.0138 Min. :0.0058 Min. : 2.487 Min. : 1.857 Min. : 0.4957 Min. : 0.0 Min. : 0.00 Min. :0.0000 Min. : 24164 Min. :1.941e+08 Min. :5.143e+11 Min. :6.027e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.790e+05 Min. :1.225e+11 Min. :9.315e+10 Min. :2.433e+06 Min. :1.301e+03 Min. :5.484e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.001 Min. :0.012 Min. :0.005 Min. : 2.884 Min. : 1.817 Min. : 0.493 Min. : 4027 Min. :5.262e+06 Min. :2.243e+09 Min. :1.389e+06 Min. : 0.058 Min. : 0.001 Min. :0.000 Min. : 0.000 Min. :1.186e+05 Min. :3.489e+09 Min. :3.239e+09 Min. :5.853e+05 Min. :1.301e+04 Min. :1.669e+08 Min. : 0.005 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. : 0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. : 168 Min. :0.001 Min. :0.011 Min. :0.006 Min. : 2.473 Min. : 1.773 Min. : 0.494 Min. : 0.000 Min. : 0.0 Min. :0.0000 Min. : 24198 Min. :1.946e+08 Min. :5.198e+11 Min. :7.913e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.817e+05 Min. :1.231e+11 Min. :9.400e+10 Min. :3.183e+06 Min. :3.774e+03 Min. :5.528e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.001 Min. :0.009 Min. :0.010 Min. : 2.869 Min. : 1.644 Min. : 0.496 Min. : 6632 Min. :1.333e+07 Min. :7.807e+09 Min. :1.426e+06 Min. : 0.058 Min. : 0.001 Min. :0.000 Min. : 0.000 Min. :2.208e+05 Min. :1.088e+10 Min. :1.331e+10 Min. :5.746e+05 Min. :3.000e+00 Min. :5.871e+08 Min. : 0.005 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. : 260.4 Min. :0.001 Min. :0.008 Min. :0.011 Min. : 2.577 Min. : 1.617 Min. : 0.497 Min. : 0.000 Min. : 0.000 Min. :0.00 Min. : 24236 Min. :1.956e+08 Min. :5.224e+11 Min. :4.696e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.861e+05 Min. :1.254e+11 Min. :9.477e+10 Min. :1.895e+06 Min. :5.507e+03 Min. :5.553e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.001 Min. :0.008 Min. :0.011 Min. : 2.866 Min. : 2.056 Min. : 0.496 Min. : 6155 Min. :1.161e+07 Min. :6.600e+09 Min. :8.006e+05 Min. : 0.058 Min. :0.001 Min. :0.000 Min. : 0.000 Min. :1.955e+05 Min. :9.552e+09 Min. :8.451e+09 Min. :3.227e+05 Min. :8.210e+02 Min. :4.207e+08 Min. : 0.005 Min. : 0.000 Min. : 0.0 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. : 234.8 Min. :0.001 Min. :0.008 Min. :0.013 Min. : 2.538 Min. : 1.980 Min. : 0.496 Min. : 0.00 Min. : 0.00 Min. :0.0000 Min. : 24358 Min. :1.971e+08 Min. :5.177e+11 Min. :7.396e+06 Min. : 0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.850e+05 Min. :1.250e+11 Min. :9.408e+10 Min. :2.975e+06 Min. :4.763e+03 Min. :5.566e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0 Min. : 1023 Min. :0.001 Min. :0.008 Min. :0.012 Min. : 2.853 Min. : 2.037 Min. : 0.496 Min. : 6900 Min. :1.522e+07 Min. :1.075e+10 Min. :2.231e+06 Min. : 0.057 Min. :0.001 Min. :0.000 Min. : 0.000 Min. :2.044e+05 Min. :1.033e+10 Min. :1.034e+10 Min. :8.939e+05 Min. :2.507e+03 Min. :5.106e+08 Min. : 0.004 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. : 292.1 Min. :0.001 Min. :0.007 Min. :0.014 Min. : 2.523 Min. : 1.955 Min. : 0.496 Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. : 24313 Min. :1.960e+08 Min. :5.149e+11 Min. :5.901e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.911e+05 Min. :1.255e+11 Min. :9.856e+10 Min. :2.436e+06 Min. :2.860e+03 Min. :5.679e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.006 Min. :0.007 Min. :0.018 Min. : 2.930 Min. : 1.947 Min. : 1.077 Min. : 7722 Min. :1.917e+07 Min. :1.546e+10 Min. :2.261e+06 Min. :0.057 Min. :0.001 Min. :0.000 Min. : 0.000 Min. :2.348e+05 Min. :1.442e+10 Min. :1.161e+10 Min. :9.168e+05 Min. :6.445e+03 Min. :6.453e+08 Min. : 0.004 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. : 291.9 Min. :0.005 Min. :0.007 Min. :0.018 Min. : 2.610 Min. : 1.921 Min. : 1.063 Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. : 24284 Min. :1.955e+08 Min. :5.167e+11 Min. :4.143e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.883e+05 Min. :1.246e+11 Min. :9.780e+10 Min. :1.767e+06 Min. :5.470e+02 Min. :5.625e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.005 Min. :0.010 Min. :0.020 Min. : 2.942 Min. : 2.154 Min. : 1.051 Min. : 6023 Min. :1.180e+07 Min. :7.555e+09 Min. :1.376e+06 Min. :0.057 Min. :0.001 Min. :0.000 Min. : 0.000 Min. :1.750e+05 Min. :7.599e+09 Min. :7.022e+09 Min. :5.570e+05 Min. :8.525e+03 Min. :3.736e+08 Min. : 0.004 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0 Min. : 256.5 Min. :0.004 Min. :0.010 Min. :0.020 Min. : 2.620 Min. : 2.106 Min. : 1.041 Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. : 24584 Min. :1.970e+08 Min. :5.129e+11 Min. :4.052e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :7.117e+05 Min. :1.303e+11 Min. :1.047e+11 Min. :1.669e+06 Min. :3.899e+03 Min. :5.953e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0 Min. : 1023 Min. :0.004 Min. :0.011 Min. :0.025 Min. : 2.949 Min. : 2.144 Min. : 1.060 Min. : 7987 Min. :2.102e+07 Min. :1.388e+10 Min. :1.586e+06 Min. : 0.056 Min. : 0.001 Min. :0.000 Min. : 0.000 Min. :2.621e+05 Min. :1.642e+10 Min. :1.529e+10 Min. :6.442e+05 Min. :1.606e+04 Min. :7.249e+08 Min. : 0.004 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. : 297.4 Min. :0.004 Min. :0.010 Min. :0.025 Min. : 2.598 Min. : 2.108 Min. : 1.045 Min. : 64.77 Min. : 0.000 Min. :0.00000 Min. :0.0000 Min. :0 Min. : 0.000 Min. :0.0000 Min. :0.0000 DELFWT:14132 PHDELWT:14132 Mode:logical Min. :0.2 Min. :1.9 Min. :1.4 Min. :2 Min. :0.800 Min. :-2.992e-06 Min. :0.009962 Min. :0.0003424 Min. :-2.3692 Min. : 0.2245 Min. :-3.028e-06 Min. :0.01847 Min. :0.003589 Min. :-3.3935 Min. : 0.2999 Min. :-2.206e-06 Min. :0.009009 Min. :0.0001201 Min. :-26.24490 Min. : 0.07625 Min. :-7.867e-07 Min. :0.005332 Min. :0.000403 Min. :-3.2550 Min. : 0.2407 Min. : 0 Min. : 5472 Min. : 15008 Min. :0.0000 Min. :-0.30666 Min. :0.00930 Min. :-0.2850280 Min. :0.02783 Min. :6.595e-05 Min. :0.03485 Min. :-0.08705 Min. :0.01120 Min. :-0.24222 Min. :0.03827 Min. :-0.003379 Min. :0.05494 Min. :-0.08982 Min. :0.00893 Min. :-0.095296 Min. :0.01336 Min. :-0.11559 Min. :0.01705 Min. :-0.21683 Min. :0.00345 Min. :-0.239831 Min. :0.01529 Min. :-0.005495 Min. :0.02092 Min. :-0.66520 Min. :0.01075 Min. :0.000e+00 Min. :0.000e+00 Min. :-11.2668 Min. :-0.31511 Min. :0.00918 Min. :2.5 Min. :7.1 Min. :0.5
111l CL 173 A : 1 3abl : 15 ZN : 849 1 : 424 B :2905 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 30.0 1st Qu.: 30.00 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 1.00 1st Qu.: 30.0 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 9.469 1st Qu.: 14.44 1st Qu.:1.0000 1st Qu.: 106958 1st Qu.:2.876e+09 1st Qu.:2.121e+13 1st Qu.:4.427e+10 1st Qu.:0.3173 1st Qu.:0.0275 1st Qu.:0.0007 1st Qu.:0.0021 1st Qu.:8.630e+06 1st Qu.:1.181e+13 1st Qu.:2.797e+13 1st Qu.:2.340e+10 1st Qu.:5.138e+09 1st Qu.:4.161e+11 1st Qu.: 0.1478 1st Qu.: 0.0037 1st Qu.: 0.0082 1st Qu.:0.0011 1st Qu.:0.0002 1st Qu.: 0.0202 1st Qu.:0.0007 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0e+00 1st Qu.:0 1st Qu.: 1900 1st Qu.:0.0715 1st Qu.:0.1974 1st Qu.:0.3046 1st Qu.: 5.594 1st Qu.: 3.546 1st Qu.: 2.654 1st Qu.: 62839 1st Qu.:1.048e+09 1st Qu.:4.744e+12 1st Qu.:1.340e+10 1st Qu.:0.3736 1st Qu.:0.0376 1st Qu.:0.0011 1st Qu.: 0.0033 1st Qu.:4.534e+06 1st Qu.:3.276e+12 1st Qu.:7.603e+12 1st Qu.:7.514e+09 1st Qu.:2.054e+09 1st Qu.:1.222e+11 1st Qu.: 0.2201 1st Qu.: 0.0084 1st Qu.: 0.0169 1st Qu.: 0.0020 1st Qu.: 0.0006 1st Qu.: 0.0343 1st Qu.:0.0014 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 1263 1st Qu.:0.0678 1st Qu.:0.1912 1st Qu.:0.2997 1st Qu.: 5.086 1st Qu.: 3.263 1st Qu.: 2.4659 1st Qu.: 8.581 1st Qu.: 12.77 1st Qu.:1.0000 1st Qu.: 99934 1st Qu.:2.480e+09 1st Qu.:1.702e+13 1st Qu.:3.533e+10 1st Qu.:0.3104 1st Qu.:0.0267 1st Qu.:0.0007 1st Qu.:0.0019 1st Qu.:7.759e+06 1st Qu.:9.470e+12 1st Qu.:2.146e+13 1st Qu.:1.857e+10 1st Qu.:3.874e+09 1st Qu.:3.399e+11 1st Qu.: 0.1405 1st Qu.: 0.0034 1st Qu.: 0.0072 1st Qu.:0.0010 1st Qu.:0.0002 1st Qu.: 0.0184 1st Qu.:0.0007 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1829 1st Qu.:0.0687 1st Qu.:0.1929 1st Qu.:0.2994 1st Qu.: 5.444 1st Qu.: 3.499 1st Qu.: 2.6289 1st Qu.: 61609 1st Qu.:1.002e+09 1st Qu.:4.474e+12 1st Qu.:1.122e+10 1st Qu.:0.3578 1st Qu.:0.0353 1st Qu.:0.0010 1st Qu.: 0.0027 1st Qu.:4.245e+06 1st Qu.:2.887e+12 1st Qu.:6.328e+12 1st Qu.:6.274e+09 1st Qu.:1.645e+09 1st Qu.:1.122e+11 1st Qu.: 0.1991 1st Qu.: 0.0072 1st Qu.: 0.014 1st Qu.: 0.0015 1st Qu.: 0.0004 1st Qu.: 0.0295 1st Qu.:0.0013 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 1279.76 1st Qu.:0.0653 1st Qu.:0.1865 1st Qu.:0.2938 1st Qu.: 4.912 1st Qu.: 3.223 1st Qu.: 2.4489 1st Qu.: 5.98 1st Qu.: 9.488 1st Qu.:1.0000 1st Qu.: 87509 1st Qu.:1.981e+09 1st Qu.:1.221e+13 1st Qu.:2.353e+10 1st Qu.:0.2974 1st Qu.:0.0255 1st Qu.:0.0006 1st Qu.: 0.0016 1st Qu.:6.300e+06 1st Qu.:6.696e+12 1st Qu.:1.390e+13 1st Qu.:1.204e+10 1st Qu.:2.229e+09 1st Qu.:2.418e+11 1st Qu.: 0.1256 1st Qu.: 0.0030 1st Qu.: 0.0053 1st Qu.: 0.0008 1st Qu.: 0.0001 1st Qu.: 0.0154 1st Qu.:0.0007 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0 1st Qu.: 1721 1st Qu.:0.0625 1st Qu.:0.1858 1st Qu.:0.2851 1st Qu.: 5.152 1st Qu.: 3.424 1st Qu.: 2.6169 1st Qu.: 60814 1st Qu.:9.935e+08 1st Qu.:4.544e+12 1st Qu.:8.991e+09 1st Qu.:0.3269 1st Qu.:0.0304 1st Qu.:0.0008 1st Qu.: 0.0017 1st Qu.:3.855e+06 1st Qu.:2.554e+12 1st Qu.:4.996e+12 1st Qu.:4.610e+09 1st Qu.:1.031e+09 1st Qu.:9.700e+10 1st Qu.: 0.1632 1st Qu.: 0.0050 1st Qu.: 0.009 1st Qu.: 0.0009 1st Qu.: 0.0002 1st Qu.: 0.0213 1st Qu.:0.0011 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 1345.6 1st Qu.:0.0602 1st Qu.:0.1798 1st Qu.:0.2815 1st Qu.: 4.652 1st Qu.: 3.168 1st Qu.: 2.4482 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.:0.0000 1st Qu.: 80446 1st Qu.:1.677e+09 1st Qu.:9.703e+12 1st Qu.:1.576e+10 1st Qu.:0.286 1st Qu.:0.024 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:5.225e+06 1st Qu.:4.915e+12 1st Qu.:9.243e+12 1st Qu.:7.840e+09 1st Qu.:1.139e+09 1st Qu.:1.751e+11 1st Qu.: 0.113 1st Qu.: 0.003 1st Qu.: 0.004 1st Qu.: 0.001 1st Qu.: 0.000 1st Qu.: 0.013 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1641 1st Qu.:0.058 1st Qu.:0.178 1st Qu.:0.276 1st Qu.: 4.844 1st Qu.: 3.364 1st Qu.: 2.599 1st Qu.: 60306 1st Qu.:9.973e+08 1st Qu.:4.631e+12 1st Qu.:6.872e+09 1st Qu.: 0.297 1st Qu.: 0.025 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:3.494e+06 1st Qu.:2.266e+12 1st Qu.:3.813e+12 1st Qu.:3.509e+09 1st Qu.:5.793e+08 1st Qu.:8.407e+10 1st Qu.: 0.131 1st Qu.: 0.003 1st Qu.: 0.01 1st Qu.: 0.001 1st Qu.: 0.000 1st Qu.: 0.016 1st Qu.:0.001 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 1421 1st Qu.:0.057 1st Qu.:0.173 1st Qu.:0.273 1st Qu.: 4.382 1st Qu.: 3.117 1st Qu.: 2.435 1st Qu.: 0.000 1st Qu.: 0.0 1st Qu.:0.0000 1st Qu.: 71079 1st Qu.:1.326e+09 1st Qu.:6.878e+12 1st Qu.:1.046e+10 1st Qu.:0.279 1st Qu.:0.024 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:4.357e+06 1st Qu.:3.416e+12 1st Qu.:6.267e+12 1st Qu.:4.954e+09 1st Qu.:6.858e+08 1st Qu.:1.308e+11 1st Qu.: 0.105 1st Qu.: 0.002 1st Qu.: 0.003 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.011 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1532 1st Qu.:0.055 1st Qu.:0.174 1st Qu.:0.267 1st Qu.: 4.606 1st Qu.: 3.294 1st Qu.: 2.562 1st Qu.: 59308 1st Qu.:9.687e+08 1st Qu.:4.582e+12 1st Qu.:5.444e+09 1st Qu.: 0.275 1st Qu.: 0.022 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:3.176e+06 1st Qu.:1.856e+12 1st Qu.:2.981e+12 1st Qu.:2.664e+09 1st Qu.:3.907e+08 1st Qu.:7.490e+10 1st Qu.: 0.111 1st Qu.: 0.002 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.012 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 1439.6 1st Qu.:0.054 1st Qu.:0.168 1st Qu.:0.265 1st Qu.: 4.201 1st Qu.: 3.066 1st Qu.: 2.413 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.00 1st Qu.: 63638 1st Qu.:1.119e+09 1st Qu.:5.454e+12 1st Qu.:7.214e+09 1st Qu.:0.273 1st Qu.:0.023 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:3.620e+06 1st Qu.:2.433e+12 1st Qu.:4.051e+12 1st Qu.:3.319e+09 1st Qu.:3.608e+08 1st Qu.:9.492e+10 1st Qu.: 0.099 1st Qu.: 0.002 1st Qu.: 0.003 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.010 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1461 1st Qu.:0.054 1st Qu.:0.169 1st Qu.:0.273 1st Qu.: 4.408 1st Qu.: 3.226 1st Qu.: 2.530 1st Qu.: 58067 1st Qu.:9.551e+08 1st Qu.:4.472e+12 1st Qu.:4.431e+09 1st Qu.: 0.253 1st Qu.:0.019 1st Qu.:0.000 1st Qu.: 0.001 1st Qu.:2.854e+06 1st Qu.:1.644e+12 1st Qu.:2.343e+12 1st Qu.:2.080e+09 1st Qu.:2.396e+08 1st Qu.:6.389e+10 1st Qu.: 0.094 1st Qu.: 0.002 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.009 1st Qu.:0.001 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 1473.5 1st Qu.:0.053 1st Qu.:0.163 1st Qu.:0.271 1st Qu.: 4.032 1st Qu.: 3.000 1st Qu.: 2.393 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:0.0000 1st Qu.: 57285 1st Qu.:9.262e+08 1st Qu.:4.274e+12 1st Qu.:4.645e+09 1st Qu.: 0.266 1st Qu.:0.022 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:2.981e+06 1st Qu.:1.764e+12 1st Qu.:2.516e+12 1st Qu.:2.130e+09 1st Qu.:2.076e+08 1st Qu.:6.809e+10 1st Qu.: 0.093 1st Qu.: 0.002 1st Qu.: 0.002 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.009 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1401 1st Qu.:0.056 1st Qu.:0.166 1st Qu.:0.287 1st Qu.: 4.190 1st Qu.: 3.164 1st Qu.: 2.517 1st Qu.: 56014 1st Qu.:9.081e+08 1st Qu.:4.089e+12 1st Qu.:3.213e+09 1st Qu.: 0.234 1st Qu.:0.017 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:2.507e+06 1st Qu.:1.371e+12 1st Qu.:1.719e+12 1st Qu.:1.521e+09 1st Qu.:1.409e+08 1st Qu.:5.238e+10 1st Qu.: 0.080 1st Qu.: 0.001 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.007 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.: 1485.3 1st Qu.:0.056 1st Qu.:0.162 1st Qu.:0.288 1st Qu.: 3.841 1st Qu.: 2.954 1st Qu.: 2.384 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 51008 1st Qu.:7.495e+08 1st Qu.:3.256e+12 1st Qu.:2.976e+09 1st Qu.:0.259 1st Qu.:0.021 1st Qu.:0.001 1st Qu.: 0.000 1st Qu.:2.419e+06 1st Qu.:1.272e+12 1st Qu.:1.559e+12 1st Qu.:1.351e+09 1st Qu.:1.110e+08 1st Qu.:4.633e+10 1st Qu.: 0.087 1st Qu.: 0.002 1st Qu.: 0.002 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.008 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1348 1st Qu.:0.061 1st Qu.:0.171 1st Qu.:0.321 1st Qu.: 3.990 1st Qu.: 3.089 1st Qu.: 2.522 1st Qu.: 53459 1st Qu.:8.456e+08 1st Qu.:3.990e+12 1st Qu.:2.255e+09 1st Qu.:0.220 1st Qu.:0.015 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:2.225e+06 1st Qu.:1.118e+12 1st Qu.:1.272e+12 1st Qu.:1.056e+09 1st Qu.:8.450e+07 1st Qu.:4.341e+10 1st Qu.: 0.069 1st Qu.: 0.001 1st Qu.: 0.001 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.006 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1511.6 1st Qu.:0.061 1st Qu.:0.165 1st Qu.:0.318 1st Qu.: 3.670 1st Qu.: 2.887 1st Qu.: 2.384 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 46145 1st Qu.:6.309e+08 1st Qu.:2.522e+12 1st Qu.:1.987e+09 1st Qu.:0.255 1st Qu.:0.021 1st Qu.:0.001 1st Qu.: 0.000 1st Qu.:2.023e+06 1st Qu.:9.101e+11 1st Qu.:1.081e+12 1st Qu.:8.812e+08 1st Qu.:6.662e+07 1st Qu.:3.486e+10 1st Qu.: 0.082 1st Qu.: 0.002 1st Qu.: 0.002 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.007 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1298 1st Qu.:0.071 1st Qu.:0.170 1st Qu.:0.371 1st Qu.: 3.840 1st Qu.: 3.024 1st Qu.: 2.518 1st Qu.: 52102 1st Qu.:8.038e+08 1st Qu.:3.678e+12 1st Qu.:1.671e+09 1st Qu.:0.211 1st Qu.:0.013 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:2.021e+06 1st Qu.:9.610e+11 1st Qu.:1.025e+12 1st Qu.:7.524e+08 1st Qu.:5.455e+07 1st Qu.:3.837e+10 1st Qu.: 0.062 1st Qu.: 0.001 1st Qu.: 0.001 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.005 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0 1st Qu.: 1531.5 1st Qu.:0.071 1st Qu.:0.162 1st Qu.:0.369 1st Qu.: 3.547 1st Qu.: 2.827 1st Qu.: 2.378 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 43744 1st Qu.:5.732e+08 1st Qu.:2.284e+12 1st Qu.:1.446e+09 1st Qu.:0.251 1st Qu.:0.020 1st Qu.:0.001 1st Qu.: 0.000 1st Qu.:1.885e+06 1st Qu.:8.044e+11 1st Qu.:8.956e+11 1st Qu.:6.424e+08 1st Qu.:4.822e+07 1st Qu.:3.039e+10 1st Qu.: 0.080 1st Qu.: 0.002 1st Qu.: 0.002 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.007 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0 1st Qu.: 1283 1st Qu.:0.083 1st Qu.:0.164 1st Qu.:0.415 1st Qu.: 3.771 1st Qu.: 2.979 1st Qu.: 2.513 1st Qu.: 50807 1st Qu.:7.782e+08 1st Qu.:3.589e+12 1st Qu.:1.327e+09 1st Qu.: 0.205 1st Qu.: 0.013 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:1.936e+06 1st Qu.:8.855e+11 1st Qu.:9.161e+11 1st Qu.:6.017e+08 1st Qu.:4.189e+07 1st Qu.:3.637e+10 1st Qu.: 0.058 1st Qu.: 0.001 1st Qu.: 0.001 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.004 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 1565.5 1st Qu.:0.082 1st Qu.:0.156 1st Qu.:0.416 1st Qu.: 3.502 1st Qu.: 2.793 1st Qu.: 2.379 1st Qu.: 222.94 1st Qu.: 9.469 1st Qu.:0.02630 1st Qu.:0.1127 1st Qu.:0 1st Qu.: 0.819 1st Qu.:0.1811 1st Qu.:1.0000 NA NA NA’s:14132 1st Qu.:0.2 1st Qu.:1.9 1st Qu.:1.4 1st Qu.:2 1st Qu.:1.800 1st Qu.:-7.560e-11 1st Qu.:0.209427 1st Qu.:0.1013822 1st Qu.:-1.1336 1st Qu.: 1.7394 1st Qu.:-6.600e-11 1st Qu.:0.20189 1st Qu.:0.103773 1st Qu.:-1.0312 1st Qu.: 1.6707 1st Qu.:-1.104e-10 1st Qu.:0.099798 1st Qu.:0.0220144 1st Qu.: -0.89931 1st Qu.: 1.29550 1st Qu.:-3.450e-11 1st Qu.:0.188691 1st Qu.:0.094683 1st Qu.:-0.9041 1st Qu.: 1.5904 1st Qu.: 303069 1st Qu.: 316088 1st Qu.: 522748 1st Qu.:0.2312 1st Qu.:-0.00873 1st Qu.:0.08642 1st Qu.:-0.1686900 1st Qu.:0.16439 1st Qu.:7.875e-02 1st Qu.:0.28159 1st Qu.:-0.00762 1st Qu.:0.05653 1st Qu.:-0.15855 1st Qu.:0.14448 1st Qu.: 0.073746 1st Qu.:0.28148 1st Qu.:-0.00317 1st Qu.:0.08416 1st Qu.:-0.028594 1st Qu.:0.10356 1st Qu.: 0.00600 1st Qu.:0.11254 1st Qu.:-0.00675 1st Qu.:0.03232 1st Qu.:-0.143829 1st Qu.:0.12733 1st Qu.: 0.066065 1st Qu.:0.26695 1st Qu.:-0.33681 1st Qu.:0.12728 1st Qu.:0.000e+00 1st Qu.:0.000e+00 1st Qu.: 0.3844 1st Qu.:-0.00788 1st Qu.:0.08475 1st Qu.:2.5 1st Qu.:7.1 1st Qu.:0.5
115l BME 901 A: 1 3abk : 14 GOL : 778 501 : 417 C : 795 Median : NA Median : NA Median : NA Median : NA Median : NA Median : NA Median : NA Median : NA Median : NA Median : NA Median : NA Median : 7.00 Median : 7.00 Median : 7.00 Median : 55.0 Median : 52.00 Median : 3.000 Median : 0.00 Median : 3.000 Median :0.0000 Median : 7.00 Median : 56.0 Median : 3.00 Median : 0.000 Median : 3.000 Median :0.0000 Median : 18.614 Median : 27.30 Median :1.0000 Median : 392324 Median :3.374e+10 Median :7.154e+14 Median :1.747e+12 Median :0.4826 Median :0.0508 Median :0.0012 Median :0.0129 Median :6.683e+07 Median :5.855e+14 Median :2.106e+15 Median :1.004e+12 Median :3.677e+11 Median :1.259e+13 Median : 0.3602 Median : 0.0167 Median : 0.0633 Median :0.0074 Median :0.0026 Median : 0.0873 Median :0.0014 Median :0.0000 Median :0.0000 Median :0.0000 Median :0e+00 Median :0 Median : 3580 Median :0.1328 Median :0.3432 Median :0.5061 Median : 8.634 Median : 4.749 Median : 3.275 Median : 201961 Median :8.933e+09 Median :1.036e+14 Median :4.992e+11 Median :0.6608 Median :0.0950 Median :0.0030 Median : 0.0331 Median :3.292e+07 Median :1.356e+14 Median :5.501e+14 Median :3.324e+11 Median :1.688e+11 Median :3.315e+12 Median : 0.7094 Median : 0.0617 Median : 0.2383 Median : 0.0212 Median : 0.0092 Median : 0.2346 Median :0.0035 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median : 2421 Median :0.1277 Median :0.3387 Median :0.5086 Median : 8.278 Median : 4.407 Median : 3.0316 Median : 17.561 Median : 24.51 Median :1.0000 Median : 371987 Median :3.003e+10 Median :5.893e+14 Median :1.510e+12 Median :0.4898 Median :0.0516 Median :0.0012 Median :0.0133 Median :6.310e+07 Median :4.979e+14 Median :1.913e+15 Median :8.845e+11 Median :3.127e+11 Median :1.150e+13 Median : 0.3698 Median : 0.0172 Median : 0.0669 Median :0.0077 Median :0.0028 Median : 0.0905 Median :0.0014 Median :0.0000 Median :0.000 Median :0.0000 Median :0.000 Median :0 Median : 3438 Median :0.1310 Median :0.3406 Median :0.5083 Median : 8.615 Median : 4.687 Median : 3.2292 Median : 202072 Median :8.784e+09 Median :9.831e+13 Median :4.894e+11 Median :0.6567 Median :0.0935 Median :0.0029 Median : 0.0320 Median :3.248e+07 Median :1.308e+14 Median :5.311e+14 Median :3.206e+11 Median :1.575e+11 Median :3.324e+12 Median : 0.6992 Median : 0.0591 Median : 0.234 Median : 0.0207 Median : 0.0089 Median : 0.2290 Median :0.0035 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median : 2417.00 Median :0.1259 Median :0.3374 Median :0.5097 Median : 8.306 Median : 4.348 Median : 2.9975 Median : 14.92 Median : 18.472 Median :1.0000 Median : 353222 Median :2.632e+10 Median :4.586e+14 Median :1.271e+12 Median :0.5084 Median :0.0541 Median :0.0013 Median : 0.0147 Median :5.957e+07 Median :4.354e+14 Median :1.765e+15 Median :7.309e+11 Median :2.667e+11 Median :1.056e+13 Median : 0.3991 Median : 0.0192 Median : 0.0794 Median : 0.0086 Median : 0.0031 Median : 0.1038 Median :0.0016 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median :0 Median : 3199 Median :0.1240 Median :0.3398 Median :0.5108 Median : 8.686 Median : 4.593 Median : 3.1510 Median : 217227 Median :9.850e+09 Median :1.054e+14 Median :5.136e+11 Median :0.6472 Median :0.0889 Median :0.0027 Median : 0.0303 Median :3.677e+07 Median :1.554e+14 Median :7.150e+14 Median :3.379e+11 Median :1.608e+11 Median :4.074e+12 Median : 0.6657 Median : 0.0525 Median : 0.219 Median : 0.0196 Median : 0.0080 Median : 0.2166 Median :0.0034 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median : 2471.8 Median :0.1191 Median :0.3397 Median :0.5135 Median : 8.412 Median : 4.310 Median : 2.9366 Median : 12.2 Median : 13.93 Median :1.0000 Median : 332182 Median :2.274e+10 Median :3.814e+14 Median :1.155e+12 Median :0.523 Median :0.056 Median :0.001 Median : 0.016 Median :5.614e+07 Median :3.576e+14 Median :1.544e+15 Median :6.599e+11 Median :2.269e+11 Median :9.433e+12 Median : 0.420 Median : 0.021 Median : 0.089 Median : 0.009 Median : 0.003 Median : 0.114 Median :0.002 Median :0.000 Median :0.00 Median :0.000 Median :0.000 Median :0 Median : 2992 Median :0.121 Median :0.346 Median :0.518 Median : 8.678 Median : 4.500 Median : 3.088 Median : 228546 Median :1.021e+10 Median :1.089e+14 Median :5.777e+11 Median : 0.628 Median : 0.084 Median :0.002 Median : 0.028 Median :3.763e+07 Median :1.593e+14 Median :7.299e+14 Median :3.627e+11 Median :1.751e+11 Median :4.446e+12 Median : 0.630 Median : 0.047 Median : 0.20 Median : 0.018 Median : 0.008 Median : 0.200 Median :0.003 Median :0.000 Median : 0.000 Median :0.00 Median :0.000 Median :0.000 Median : 2517 Median :0.117 Median :0.347 Median :0.521 Median : 8.473 Median : 4.238 Median : 2.885 Median : 9.215 Median : 10.6 Median :1.0000 Median : 280935 Median :1.629e+10 Median :2.224e+14 Median :7.137e+11 Median :0.525 Median :0.056 Median :0.001 Median : 0.015 Median :4.370e+07 Median :2.246e+14 Median :9.829e+14 Median :4.405e+11 Median :1.563e+11 Median :6.147e+12 Median : 0.429 Median : 0.021 Median : 0.091 Median : 0.009 Median : 0.003 Median : 0.116 Median :0.002 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median :0 Median : 2709 Median :0.119 Median :0.350 Median :0.527 Median : 8.460 Median : 4.360 Median : 3.012 Median : 208895 Median :8.384e+09 Median :8.358e+13 Median :4.365e+11 Median : 0.594 Median : 0.076 Median :0.002 Median : 0.024 Median :3.302e+07 Median :1.173e+14 Median :6.077e+14 Median :2.828e+11 Median :1.362e+11 Median :3.757e+12 Median : 0.569 Median : 0.040 Median : 0.16 Median : 0.015 Median : 0.006 Median : 0.172 Median :0.003 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median : 2479.6 Median :0.114 Median :0.348 Median :0.533 Median : 8.245 Median : 4.104 Median : 2.825 Median : 3.495 Median : 8.224 Median :1.00 Median : 235095 Median :1.149e+10 Median :1.206e+14 Median :4.385e+11 Median :0.516 Median :0.053 Median :0.001 Median 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115l CL 173 A : 1 2afh : 11 CA : 661 401 : 395 D : 552 Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean : 13.59 Mean : 13.43 Mean : 13.05 Mean :101.5 Mean : 98.33 Mean : 7.332 Mean : 1.35 Mean : 3.817 Mean :0.2208 Mean : 13.74 Mean :103.8 Mean : 7.47 Mean : 1.357 Mean : 3.979 Mean :0.2213 Mean : 31.535 Mean : 51.45 Mean :0.9725 Mean : 3091661 Mean :1.622e+13 Mean :1.938e+20 Mean :2.123e+16 Mean :0.5752 Mean :0.0782 Mean :0.0026 Mean :0.0651 Mean :4.023e+09 Mean :1.143e+20 Mean :6.415e+20 Mean :1.253e+16 Mean :6.720e+15 Mean :1.592e+17 Mean : 0.6958 Mean : 0.1159 Mean : 1.0010 Mean :0.0416 Mean :0.0260 Mean : 0.4266 Mean :0.0022 Mean :0.0000 Mean :0.0000 Mean :0.0000 Mean :0e+00 Mean :0 Mean : 6622 Mean :0.2174 Mean :0.4061 Mean :0.5133 Mean :10.851 Mean : 5.807 Mean : 3.682 Mean : 1670086 Mean :3.188e+12 Mean :4.092e+18 Mean :3.482e+15 Mean :0.7665 Mean :0.1466 Mean :0.0071 Mean : 0.2521 Mean :2.036e+09 Mean :1.025e+19 Mean :7.811e+19 Mean :2.193e+15 Mean :1.334e+15 Mean :2.463e+16 Mean : 1.4490 Mean : 0.6412 Mean : 6.6229 Mean : 0.1849 Mean : 0.1401 Mean : 1.4628 Mean :0.0078 Mean :0.0000 Mean :0.0004 Mean :0.0001 Mean :0.0001 Mean :0.0000 Mean : 4059 Mean :0.2206 Mean :0.4058 Mean :0.5151 Mean :10.400 Mean : 5.487 Mean : 3.4481 Mean : 29.751 Mean : 46.67 Mean :0.9522 Mean : 2894498 Mean :1.384e+13 Mean :1.558e+20 Mean :1.829e+16 Mean :0.5905 Mean :0.0823 Mean :0.0028 Mean :0.0756 Mean :3.739e+09 Mean :9.661e+19 Mean :5.336e+20 Mean :1.094e+16 Mean :6.034e+15 Mean :1.347e+17 Mean : 0.7492 Mean : 0.1351 Mean : 1.2288 Mean :0.0491 Mean :0.0315 Mean : 0.4913 Mean :0.0024 Mean :0.0000 Mean :0.000 Mean :0.0000 Mean :0.000 Mean :0 Mean : 6285 Mean :0.2203 Mean :0.4073 Mean :0.5139 Mean :10.825 Mean : 5.750 Mean : 3.6288 Mean : 1627744 Mean :2.936e+12 Mean :3.562e+18 Mean :3.237e+15 Mean :0.7698 Mean :0.1473 Mean :0.0071 Mean : 0.2734 Mean :1.973e+09 Mean :9.264e+18 Mean :7.123e+19 Mean :2.063e+15 Mean 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1.5867 Mean : 0.8918 Mean : 10.868 Mean : 0.2686 Mean : 0.2157 Mean : 1.8945 Mean :0.0081 Mean :0.0000 Mean :0.0005 Mean :0.0001 Mean :0.0001 Mean :0.0000 Mean : 3919.5 Mean :0.2311 Mean :0.4116 Mean :0.5172 Mean :10.432 Mean : 5.387 Mean : 3.3448 Mean : 21.1 Mean : 28.00 Mean :0.7844 Mean : 2143093 Mean :6.474e+12 Mean :5.200e+19 Mean :9.143e+15 Mean :0.664 Mean :0.105 Mean :0.004 Mean : 0.147 Mean :2.643e+09 Mean :4.120e+19 Mean :2.049e+20 Mean :5.802e+15 Mean :3.575e+15 Mean :5.861e+16 Mean : 1.043 Mean : 0.320 Mean : 3.088 Mean : 0.100 Mean : 0.068 Mean : 0.928 Mean :0.004 Mean :0.000 Mean :0.00 Mean :0.000 Mean :0.000 Mean :0 Mean : 5042 Mean :0.236 Mean :0.417 Mean :0.518 Mean :10.758 Mean : 5.557 Mean : 3.458 Mean : 1416666 Mean :1.856e+12 Mean :1.666e+18 Mean :2.191e+15 Mean : 0.780 Mean : 0.153 Mean :0.007 Mean : 0.434 Mean :1.652e+09 Mean :5.106e+18 Mean :4.232e+19 Mean :1.487e+15 Mean :1.019e+15 Mean :1.456e+16 Mean : 1.669 Mean : 1.234 Mean : 19.76 Mean : 0.334 Mean : 0.268 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Mean :0.000 Mean : 3618.9 Mean :0.246 Mean :0.423 Mean :0.526 Mean :10.167 Mean : 5.141 Mean : 3.188 Mean : 13.862 Mean : 16.100 Mean :0.59 Mean : 1489327 Mean :2.966e+12 Mean :1.528e+19 Mean :4.724e+15 Mean :0.699 Mean :0.116 Mean :0.004 Mean : 0.253 Mean :1.726e+09 Mean :1.838e+19 Mean :8.501e+19 Mean :3.188e+15 Mean :2.164e+15 Mean :2.655e+16 Mean : 1.268 Mean : 0.495 Mean : 7.081 Mean : 0.193 Mean : 0.153 Mean : 1.495 Mean :0.005 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0 Mean : 4002 Mean :0.251 Mean :0.427 Mean :0.532 Mean :10.218 Mean : 5.179 Mean : 3.254 Mean : 1092621 Mean :9.870e+11 Mean :6.227e+17 Mean :1.339e+15 Mean : 0.753 Mean :0.141 Mean :0.006 Mean : 1.045 Mean :1.196e+09 Mean :2.495e+18 Mean :2.148e+19 Mean :9.911e+14 Mean :7.593e+14 Mean :7.930e+15 Mean : 1.737 Mean : 0.956 Mean : 73.8 Mean : 0.958 Mean : 0.900 Mean : 3.705 Mean :0.009 Mean :0.000 Mean : 0.009 Mean :0.001 Mean :0.001 Mean :0.000 Mean : 3446.0 Mean :0.256 Mean :0.428 Mean :0.537 Mean : 9.854 Mean : 4.944 Mean : 3.097 Mean : 10.88 Mean : 11.94 Mean :0.4852 Mean : 1222842 Mean :2.019e+12 Mean :8.302e+18 Mean :3.378e+15 Mean : 0.687 Mean :0.112 Mean :0.004 Mean : 0.278 Mean :1.350e+09 Mean :1.269e+19 Mean :5.687e+19 Mean :2.295e+15 Mean :1.573e+15 Mean :1.828e+16 Mean : 1.299 Mean : 1.250 Mean : 12.227 Mean : 0.224 Mean : 0.187 Mean : 1.879 Mean :0.005 Mean :0.000 Mean :0.000 Mean :0.00 Mean :0.000 Mean :0 Mean : 3606 Mean :0.266 Mean :0.437 Mean :0.550 Mean : 9.751 Mean : 4.941 Mean : 3.170 Mean : 926558 Mean :6.708e+11 Mean :3.182e+17 Mean :9.803e+14 Mean : 0.717 Mean :0.129 Mean :0.005 Mean : 0.478 Mean :9.550e+08 Mean :1.601e+18 Mean :1.355e+19 Mean :7.529e+14 Mean :6.013e+14 Mean :5.339e+15 Mean : 1.628 Mean : 3.869 Mean : 31.36 Mean : 0.415 Mean : 0.372 Mean : 2.996 Mean :0.008 Mean :0.000 Mean :0.002 Mean :0.000 Mean :0.00 Mean :0.000 Mean : 3285.4 Mean :0.271 Mean :0.439 Mean :0.555 Mean : 9.395 Mean : 4.716 Mean : 3.018 Mean : 8.499 Mean : 8.825 Mean :0.3972 Mean : 980603 Mean :1.345e+12 Mean :4.072e+18 Mean :2.389e+15 Mean :0.650 Mean :0.103 Mean :0.004 Mean : 0.315 Mean :1.037e+09 Mean :8.459e+18 Mean :3.897e+19 Mean :1.649e+15 Mean :1.156e+15 Mean :1.262e+16 Mean : 1.182 Mean : 0.447 Mean : 9.429 Mean : 0.263 Mean : 0.229 Mean : 1.570 Mean :0.005 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0 Mean : 3254 Mean :0.285 Mean :0.451 Mean :0.577 Mean : 9.133 Mean : 4.684 Mean : 3.096 Mean : 761473 Mean :4.467e+11 Mean :1.706e+17 Mean :6.972e+14 Mean :0.659 Mean :0.114 Mean :0.005 Mean : 0.406 Mean :7.320e+08 Mean :1.082e+18 Mean :9.105e+18 Mean :5.415e+14 Mean :4.377e+14 Mean :3.615e+15 Mean : 1.407 Mean : 0.689 Mean : 11.904 Mean : 0.343 Mean : 0.300 Mean : 2.001 Mean :0.007 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0 Mean : 3133.6 Mean :0.290 Mean :0.453 Mean :0.582 Mean : 8.797 Mean : 4.464 Mean : 2.948 Mean : 6.594 Mean : 6.491 Mean :0.3167 Mean : 770168 Mean :8.672e+11 Mean :1.746e+18 Mean :1.586e+15 Mean :0.599 Mean 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Mean :8.853e+14 Mean :6.166e+14 Mean :5.382e+15 Mean : 0.908 Mean : 0.327 Mean : 6.447 Mean : 0.167 Mean : 0.147 Mean : 1.216 Mean :0.004 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.00 Mean :0 Mean : 2720 Mean :0.314 Mean :0.464 Mean :0.631 Mean : 7.959 Mean : 4.151 Mean : 2.954 Mean : 498566 Mean :1.960e+11 Mean :4.007e+16 Mean :3.507e+14 Mean : 0.550 Mean : 0.085 Mean :0.004 Mean : 0.349 Mean :4.013e+08 Mean :4.416e+17 Mean :4.001e+18 Mean :2.741e+14 Mean :2.230e+14 Mean :1.661e+15 Mean : 1.116 Mean : 1.051 Mean : 14.992 Mean : 0.297 Mean : 0.262 Mean : 1.927 Mean :0.006 Mean :0.000 Mean :0.001 Mean :0.000 Mean :0.000 Mean :0.000 Mean : 2900.9 Mean :0.318 Mean :0.465 Mean :0.637 Mean : 7.671 Mean : 3.948 Mean : 2.810 Mean : 937.24 Mean : 31.535 Mean :0.04484 Mean :0.1815 Mean :0 Mean : 1.690 Mean :0.3039 Mean :0.9725 NA NA NA Mean :0.2 Mean :1.9 Mean :1.4 Mean :2 Mean :2.128 Mean :-7.000e-11 Mean :0.275025 Mean :0.1927164 Mean :-0.9678 Mean : 2.7854 Mean : 2.942e-09 Mean :0.26458 Mean :0.197404 Mean :-0.8724 Mean : 2.5077 Mean :-5.264e-10 Mean :0.133948 Mean :0.0575110 Mean : -0.75040 Mean : 2.38074 Mean : 4.934e-10 Mean :0.247163 Mean :0.182585 Mean :-0.7575 Mean : 2.4045 Mean : 1615555 Mean : 745313 Mean : 1369266 Mean :0.3389 Mean :-0.00593 Mean :0.11494 Mean :-0.1417209 Mean :0.19472 Mean :8.550e-02 Mean :0.35322 Mean :-0.00488 Mean :0.07565 Mean :-0.13486 Mean :0.16813 Mean : 0.081655 Mean :0.35238 Mean :-0.00190 Mean :0.11144 Mean :-0.018502 Mean :0.13226 Mean : 0.01071 Mean :0.14801 Mean :-0.00403 Mean :0.04446 Mean :-0.123219 Mean :0.14792 Mean : 0.074781 Mean :0.33536 Mean :-0.28451 Mean :0.15577 Mean :1.348e+73 Mean :6.825e+71 Mean : 0.4472 Mean :-0.00524 Mean :0.11325 Mean :2.5 Mean :7.1 Mean :0.5
123l BME 901 A: 1 3a0b : 11 MG : 402 201 : 306 E : 221 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: 21.00 3rd Qu.: 20.00 3rd Qu.: 20.00 3rd Qu.:145.0 3rd Qu.:139.15 3rd Qu.:10.000 3rd Qu.: 2.00 3rd Qu.: 5.000 3rd Qu.:0.0000 3rd Qu.: 21.00 3rd Qu.:151.0 3rd Qu.:10.00 3rd Qu.: 2.000 3rd Qu.: 6.000 3rd Qu.:0.0000 3rd Qu.: 41.434 3rd Qu.: 67.06 3rd Qu.:1.0000 3rd Qu.: 2563883 3rd Qu.:1.258e+12 3rd Qu.:1.241e+17 3rd Qu.:2.287e+14 3rd Qu.:0.7365 3rd Qu.:0.0992 3rd Qu.:0.0028 3rd Qu.:0.0521 3rd Qu.:1.197e+09 3rd Qu.:1.535e+17 3rd Qu.:7.688e+17 3rd Qu.:1.300e+14 3rd Qu.:5.116e+13 3rd Qu.:1.647e+15 3rd Qu.: 0.8471 3rd Qu.: 0.0720 3rd Qu.: 0.4041 3rd Qu.:0.0306 3rd Qu.:0.0150 3rd Qu.: 0.3408 3rd Qu.:0.0026 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0e+00 3rd Qu.:0 3rd Qu.: 8604 3rd Qu.:0.2864 3rd Qu.:0.5983 3rd Qu.:0.7264 3rd Qu.:14.739 3rd Qu.: 7.444 3rd Qu.: 4.313 3rd Qu.: 1405891 3rd Qu.:3.835e+11 3rd Qu.:2.108e+16 3rd Qu.:7.100e+13 3rd Qu.:1.0132 3rd Qu.:0.1898 3rd Qu.:0.0074 3rd Qu.: 0.1622 3rd Qu.:6.281e+08 3rd Qu.:4.085e+16 3rd Qu.:2.124e+17 3rd Qu.:4.662e+13 3rd Qu.:2.294e+13 3rd Qu.:4.635e+14 3rd Qu.: 1.6894 3rd Qu.: 0.2874 3rd Qu.: 1.6383 3rd Qu.: 0.1056 3rd Qu.: 0.0611 3rd Qu.: 0.9441 3rd Qu.:0.0077 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 5322 3rd Qu.:0.3017 3rd Qu.:0.6051 3rd Qu.:0.7363 3rd Qu.:14.368 3rd Qu.: 7.143 3rd Qu.: 4.0015 3rd Qu.: 39.384 3rd Qu.: 61.21 3rd Qu.:1.0000 3rd Qu.: 2444647 3rd Qu.:1.136e+12 3rd Qu.:1.036e+17 3rd Qu.:2.211e+14 3rd Qu.:0.7633 3rd Qu.:0.1046 3rd Qu.:0.0029 3rd Qu.:0.0576 3rd Qu.:1.145e+09 3rd Qu.:1.387e+17 3rd Qu.:7.039e+17 3rd Qu.:1.258e+14 3rd Qu.:4.840e+13 3rd Qu.:1.513e+15 3rd Qu.: 0.9138 3rd Qu.: 0.0810 3rd Qu.: 0.4712 3rd Qu.:0.0344 3rd Qu.:0.0168 3rd Qu.: 0.3852 3rd Qu.:0.0029 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 8213 3rd Qu.:0.2989 3rd Qu.:0.6085 3rd Qu.:0.7338 3rd Qu.:14.839 3rd Qu.: 7.393 3rd Qu.: 4.2305 3rd Qu.: 1411425 3rd Qu.:3.754e+11 3rd Qu.:1.981e+16 3rd Qu.:7.614e+13 3rd Qu.:1.0250 3rd Qu.:0.1896 3rd Qu.:0.0073 3rd Qu.: 0.1652 3rd Qu.:6.229e+08 3rd Qu.:4.167e+16 3rd Qu.:2.155e+17 3rd Qu.:4.987e+13 3rd Qu.:2.406e+13 3rd Qu.:4.685e+14 3rd Qu.: 1.7199 3rd Qu.: 0.2869 3rd Qu.: 1.696 3rd Qu.: 0.1089 3rd Qu.: 0.0639 3rd Qu.: 0.9822 3rd Qu.:0.0078 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 5277.11 3rd Qu.:0.3139 3rd Qu.:0.6152 3rd Qu.:0.7444 3rd Qu.:14.466 3rd Qu.: 7.099 3rd Qu.: 3.9472 3rd Qu.: 34.14 3rd Qu.: 47.706 3rd Qu.:1.0000 3rd Qu.: 2198054 3rd Qu.:9.254e+11 3rd Qu.:7.874e+16 3rd Qu.:1.902e+14 3rd Qu.:0.8274 3rd Qu.:0.1201 3rd Qu.:0.0033 3rd Qu.: 0.0759 3rd Qu.:1.030e+09 3rd Qu.:1.117e+17 3rd Qu.:5.691e+17 3rd Qu.:1.103e+14 3rd Qu.:4.242e+13 3rd Qu.:1.216e+15 3rd Qu.: 1.0766 3rd Qu.: 0.1077 3rd Qu.: 0.6657 3rd Qu.: 0.0462 3rd Qu.: 0.0222 3rd Qu.: 0.5005 3rd Qu.:0.0035 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0 3rd Qu.: 7378 3rd Qu.:0.3368 3rd Qu.:0.6307 3rd Qu.:0.7479 3rd Qu.:15.036 3rd Qu.: 7.290 3rd Qu.: 4.1043 3rd Qu.: 1379860 3rd Qu.:3.597e+11 3rd Qu.:1.752e+16 3rd Qu.:8.023e+13 3rd Qu.:1.0468 3rd Qu.:0.1909 3rd Qu.:0.0069 3rd Qu.: 0.1823 3rd Qu.:6.298e+08 3rd Qu.:3.960e+16 3rd Qu.:2.164e+17 3rd Qu.:5.226e+13 3rd Qu.:2.598e+13 3rd Qu.:4.794e+14 3rd Qu.: 1.7974 3rd Qu.: 0.3002 3rd Qu.: 1.901 3rd Qu.: 0.1243 3rd Qu.: 0.0725 3rd Qu.: 1.0608 3rd Qu.:0.0079 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 5181.2 3rd Qu.:0.3548 3rd Qu.:0.6380 3rd Qu.:0.7573 3rd Qu.:14.648 3rd Qu.: 7.018 3rd Qu.: 3.8449 3rd Qu.: 28.5 3rd Qu.: 36.67 3rd Qu.:1.0000 3rd Qu.: 1985591 3rd Qu.:7.154e+11 3rd Qu.:4.886e+16 3rd Qu.:1.600e+14 3rd Qu.:0.884 3rd Qu.:0.134 3rd Qu.:0.004 3rd Qu.: 0.097 3rd Qu.:9.385e+08 3rd Qu.:8.713e+16 3rd Qu.:5.017e+17 3rd Qu.:9.193e+13 3rd Qu.:3.750e+13 3rd Qu.:1.022e+15 3rd Qu.: 1.213 3rd Qu.: 0.133 3rd Qu.: 0.881 3rd Qu.: 0.058 3rd Qu.: 0.029 3rd Qu.: 0.610 3rd Qu.:0.004 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 6578 3rd Qu.:0.378 3rd Qu.:0.659 3rd Qu.:0.762 3rd Qu.:15.087 3rd Qu.: 7.121 3rd Qu.: 3.971 3rd Qu.: 1320446 3rd Qu.:3.169e+11 3rd Qu.:1.409e+16 3rd Qu.:7.506e+13 3rd Qu.: 1.068 3rd Qu.: 0.189 3rd Qu.:0.007 3rd Qu.: 0.193 3rd Qu.:6.024e+08 3rd Qu.:3.541e+16 3rd Qu.:2.111e+17 3rd Qu.:4.923e+13 3rd Qu.:2.523e+13 3rd Qu.:4.485e+14 3rd Qu.: 1.859 3rd Qu.: 0.305 3rd Qu.: 2.03 3rd Qu.: 0.132 3rd Qu.: 0.079 3rd Qu.: 1.123 3rd Qu.:0.008 3rd Qu.:0.000 3rd Qu.: 0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.: 5018 3rd Qu.:0.400 3rd Qu.:0.667 3rd Qu.:0.773 3rd Qu.:14.673 3rd Qu.: 6.898 3rd Qu.: 3.738 3rd Qu.: 23.645 3rd Qu.: 27.2 3rd Qu.:1.0000 3rd Qu.: 1660848 3rd Qu.:4.777e+11 3rd Qu.:2.560e+16 3rd Qu.:9.978e+13 3rd Qu.:0.913 3rd Qu.:0.139 3rd Qu.:0.004 3rd Qu.: 0.109 3rd Qu.:7.484e+08 3rd Qu.:5.254e+16 3rd Qu.:3.179e+17 3rd Qu.:6.074e+13 3rd Qu.:2.617e+13 3rd Qu.:6.772e+14 3rd Qu.: 1.309 3rd Qu.: 0.153 3rd Qu.: 1.000 3rd Qu.: 0.069 3rd Qu.: 0.035 3rd Qu.: 0.675 3rd Qu.:0.005 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 5715 3rd Qu.:0.414 3rd Qu.:0.674 3rd Qu.:0.778 3rd Qu.:14.650 3rd Qu.: 6.874 3rd Qu.: 3.811 3rd Qu.: 1199231 3rd Qu.:2.535e+11 3rd Qu.:9.606e+15 3rd Qu.:5.416e+13 3rd Qu.: 1.080 3rd Qu.: 0.185 3rd Qu.:0.006 3rd Qu.: 0.198 3rd Qu.:5.172e+08 3rd Qu.:2.475e+16 3rd Qu.:1.516e+17 3rd Qu.:3.561e+13 3rd Qu.:1.979e+13 3rd Qu.:3.399e+14 3rd Qu.: 1.916 3rd Qu.: 0.292 3rd Qu.: 2.22 3rd Qu.: 0.136 3rd Qu.: 0.085 3rd Qu.: 1.187 3rd Qu.:0.008 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.: 4716.6 3rd Qu.:0.431 3rd Qu.:0.681 3rd Qu.:0.785 3rd Qu.:14.332 3rd Qu.: 6.642 3rd Qu.: 3.601 3rd Qu.: 19.464 3rd Qu.: 19.824 3rd Qu.:1.00 3rd Qu.: 1371273 3rd Qu.:3.140e+11 3rd Qu.:1.314e+16 3rd Qu.:5.502e+13 3rd Qu.:0.921 3rd Qu.:0.139 3rd Qu.:0.004 3rd Qu.: 0.116 3rd Qu.:5.617e+08 3rd Qu.:2.766e+16 3rd Qu.:1.793e+17 3rd Qu.:3.384e+13 3rd Qu.:1.651e+13 3rd Qu.:4.296e+14 3rd Qu.: 1.350 3rd Qu.: 0.161 3rd Qu.: 1.109 3rd Qu.: 0.074 3rd Qu.: 0.039 3rd Qu.: 0.710 3rd Qu.:0.005 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 5064 3rd Qu.:0.455 3rd Qu.:0.694 3rd Qu.:0.791 3rd Qu.:14.138 3rd Qu.: 6.550 3rd Qu.: 3.655 3rd Qu.: 1031112 3rd Qu.:1.790e+11 3rd Qu.:5.742e+15 3rd Qu.:3.455e+13 3rd Qu.: 1.052 3rd Qu.:0.176 3rd Qu.:0.006 3rd Qu.: 0.191 3rd Qu.:3.961e+08 3rd Qu.:1.451e+16 3rd Qu.:9.160e+16 3rd Qu.:2.328e+13 3rd Qu.:1.350e+13 3rd Qu.:2.183e+14 3rd Qu.: 1.817 3rd Qu.: 0.266 3rd Qu.: 2.1 3rd Qu.: 0.136 3rd Qu.: 0.089 3rd Qu.: 1.124 3rd Qu.:0.008 3rd Qu.:0.000 3rd Qu.: 0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.: 4449.9 3rd Qu.:0.468 3rd Qu.:0.697 3rd Qu.:0.799 3rd Qu.:13.871 3rd Qu.: 6.336 3rd Qu.: 3.466 3rd Qu.: 15.80 3rd Qu.: 14.36 3rd Qu.:1.0000 3rd Qu.: 1107271 3rd Qu.:2.027e+11 3rd Qu.:7.000e+15 3rd Qu.:3.081e+13 3rd Qu.: 0.883 3rd Qu.:0.130 3rd Qu.:0.003 3rd Qu.: 0.105 3rd Qu.:3.914e+08 3rd Qu.:1.424e+16 3rd Qu.:8.976e+16 3rd Qu.:1.922e+13 3rd Qu.:9.001e+12 3rd Qu.:2.430e+14 3rd Qu.: 1.261 3rd Qu.: 0.133 3rd Qu.: 0.979 3rd Qu.: 0.068 3rd Qu.: 0.038 3rd Qu.: 0.652 3rd Qu.:0.005 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 4507 3rd Qu.:0.495 3rd Qu.:0.715 3rd Qu.:0.808 3rd Qu.:13.334 3rd Qu.: 6.186 3rd Qu.: 3.528 3rd Qu.: 866913 3rd Qu.:1.323e+11 3rd Qu.:3.651e+15 3rd Qu.:2.025e+13 3rd Qu.: 1.002 3rd Qu.:0.162 3rd Qu.:0.005 3rd Qu.: 0.171 3rd Qu.:2.906e+08 3rd Qu.:8.087e+15 3rd Qu.:4.935e+16 3rd Qu.:1.365e+13 3rd Qu.:7.566e+12 3rd Qu.:1.366e+14 3rd Qu.: 1.708 3rd Qu.: 0.224 3rd Qu.: 1.79 3rd Qu.: 0.129 3rd Qu.: 0.076 3rd Qu.: 0.981 3rd Qu.:0.008 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.: 4279.1 3rd Qu.:0.511 3rd Qu.:0.721 3rd Qu.:0.816 3rd Qu.:13.031 3rd Qu.: 5.983 3rd Qu.: 3.340 3rd Qu.: 12.412 3rd Qu.: 10.976 3rd Qu.:1.0000 3rd Qu.: 814855 3rd Qu.:1.176e+11 3rd Qu.:3.385e+15 3rd Qu.:1.206e+13 3rd Qu.:0.801 3rd Qu.:0.114 3rd Qu.:0.003 3rd Qu.: 0.080 3rd Qu.:2.398e+08 3rd Qu.:5.767e+15 3rd Qu.:3.260e+16 3rd Qu.:7.591e+12 3rd Qu.:3.179e+12 3rd Qu.:1.042e+14 3rd Qu.: 1.050 3rd Qu.: 0.095 3rd Qu.: 0.639 3rd Qu.: 0.053 3rd Qu.: 0.026 3rd Qu.: 0.471 3rd Qu.:0.005 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 4013 3rd Qu.:0.537 3rd Qu.:0.740 3rd Qu.:0.826 3rd Qu.:11.845 3rd Qu.: 5.824 3rd Qu.: 3.387 3rd Qu.: 690176 3rd Qu.:8.212e+10 3rd Qu.:1.911e+15 3rd Qu.:9.179e+12 3rd Qu.:0.903 3rd Qu.:0.139 3rd Qu.:0.004 3rd Qu.: 0.127 3rd Qu.:1.923e+08 3rd Qu.:3.656e+15 3rd Qu.:2.131e+16 3rd Qu.:5.996e+12 3rd Qu.:3.316e+12 3rd Qu.:6.995e+13 3rd Qu.: 1.407 3rd Qu.: 0.158 3rd Qu.: 1.187 3rd Qu.: 0.091 3rd Qu.: 0.054 3rd Qu.: 0.741 3rd Qu.:0.007 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 4036.0 3rd Qu.:0.554 3rd Qu.:0.747 3rd Qu.:0.834 3rd Qu.:11.634 3rd Qu.: 5.615 3rd Qu.: 3.204 3rd Qu.: 8.015 3rd Qu.: 8.848 3rd Qu.:1.0000 3rd Qu.: 572611 3rd Qu.:6.305e+10 3rd Qu.:1.417e+15 3rd Qu.:4.757e+12 3rd Qu.:0.703 3rd Qu.:0.089 3rd Qu.:0.002 3rd Qu.: 0.052 3rd Qu.:1.303e+08 3rd Qu.:1.804e+15 3rd Qu.:8.163e+15 3rd Qu.:2.646e+12 3rd Qu.:1.042e+12 3rd Qu.:3.926e+13 3rd Qu.: 0.809 3rd Qu.: 0.056 3rd Qu.: 0.372 3rd Qu.: 0.032 3rd Qu.: 0.017 3rd Qu.: 0.319 3rd Qu.:0.004 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 3556 3rd Qu.:0.566 3rd Qu.:0.758 3rd Qu.:0.838 3rd Qu.:10.520 3rd Qu.: 5.438 3rd Qu.: 3.241 3rd Qu.: 522346 3rd Qu.:4.874e+10 3rd Qu.:9.783e+14 3rd Qu.:4.184e+12 3rd Qu.:0.754 3rd Qu.:0.099 3rd Qu.:0.003 3rd Qu.: 0.065 3rd Qu.:1.118e+08 3rd Qu.:1.395e+15 3rd Qu.:5.903e+15 3rd Qu.:2.501e+12 3rd Qu.:1.071e+12 3rd Qu.:2.916e+13 3rd Qu.: 0.982 3rd Qu.: 0.077 3rd Qu.: 0.550 3rd Qu.: 0.047 3rd Qu.: 0.026 3rd Qu.: 0.410 3rd Qu.:0.005 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0 3rd Qu.: 3776.1 3rd Qu.:0.584 3rd Qu.:0.761 3rd Qu.:0.846 3rd Qu.:10.263 3rd Qu.: 5.200 3rd Qu.: 3.063 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.:0.0000 3rd Qu.: 407467 3rd Qu.:3.338e+10 3rd Qu.:6.203e+14 3rd Qu.:2.083e+12 3rd Qu.:0.620 3rd Qu.:0.072 3rd Qu.:0.002 3rd Qu.: 0.033 3rd Qu.:7.534e+07 3rd Qu.:6.485e+14 3rd Qu.:2.948e+15 3rd Qu.:1.143e+12 3rd Qu.:3.898e+11 3rd Qu.:1.651e+13 3rd Qu.: 0.593 3rd Qu.: 0.036 3rd Qu.: 0.198 3rd Qu.: 0.019 3rd Qu.: 0.009 3rd Qu.: 0.198 3rd Qu.:0.003 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0 3rd Qu.: 3176 3rd Qu.:0.586 3rd Qu.:0.769 3rd Qu.:0.853 3rd Qu.: 9.419 3rd Qu.: 4.989 3rd Qu.: 3.140 3rd Qu.: 389669 3rd Qu.:3.000e+10 3rd Qu.:4.772e+14 3rd Qu.:1.779e+12 3rd Qu.: 0.608 3rd Qu.: 0.074 3rd Qu.:0.002 3rd Qu.: 0.032 3rd Qu.:6.932e+07 3rd Qu.:5.867e+14 3rd Qu.:2.249e+15 3rd Qu.:1.103e+12 3rd Qu.:4.860e+11 3rd Qu.:1.376e+13 3rd Qu.: 0.583 3rd Qu.: 0.038 3rd Qu.: 0.192 3rd Qu.: 0.020 3rd Qu.: 0.011 3rd Qu.: 0.195 3rd Qu.:0.004 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.: 3583.6 3rd Qu.:0.599 3rd Qu.:0.773 3rd Qu.:0.859 3rd Qu.: 9.171 3rd Qu.: 4.796 3rd Qu.: 2.962 3rd Qu.: 1140.48 3rd Qu.: 41.434 3rd Qu.:0.05407 3rd Qu.:0.2123 3rd Qu.:0 3rd Qu.: 2.098 3rd Qu.:0.3560 3rd Qu.:1.0000 NA NA NA 3rd Qu.:0.2 3rd Qu.:1.9 3rd Qu.:1.4 3rd Qu.:2 3rd Qu.:2.401 3rd Qu.: 4.709e-10 3rd Qu.:0.337722 3rd Qu.:0.2628045 3rd Qu.:-0.7773 3rd Qu.: 3.4233 3rd Qu.: 6.750e-10 3rd Qu.:0.32442 3rd Qu.:0.269623 3rd Qu.:-0.6866 3rd Qu.: 3.1626 3rd Qu.: 1.301e-10 3rd Qu.:0.164254 3rd Qu.:0.0715205 3rd Qu.: -0.55303 3rd Qu.: 2.95817 3rd Qu.: 4.920e-10 3rd Qu.:0.304156 3rd Qu.:0.251224 3rd Qu.:-0.5877 3rd Qu.: 3.0455 3rd Qu.: 1632553 3rd Qu.: 847645 3rd Qu.: 1577760 3rd Qu.:0.4363 3rd Qu.:-0.00172 3rd Qu.:0.14024 3rd Qu.:-0.1177243 3rd Qu.:0.22674 3rd Qu.:9.420e-02 3rd Qu.:0.42618 3rd Qu.:-0.00113 3rd Qu.:0.09062 3rd Qu.:-0.11395 3rd Qu.:0.19424 3rd Qu.: 0.091062 3rd Qu.:0.42462 3rd Qu.:-0.00012 3rd Qu.:0.13623 3rd Qu.:-0.007293 3rd Qu.:0.15861 3rd Qu.: 0.01579 3rd Qu.:0.17967 3rd Qu.:-0.00043 3rd Qu.:0.05314 3rd Qu.:-0.106279 3rd Qu.:0.17112 3rd Qu.: 0.084687 3rd Qu.:0.40525 3rd Qu.:-0.23695 3rd Qu.:0.17675 3rd Qu.:0.000e+00 3rd Qu.:0.000e+00 3rd Qu.: 0.5041 3rd Qu.:-0.00096 3rd Qu.:0.13851 3rd Qu.:2.5 3rd Qu.:7.1 3rd Qu.:0.5
123l CL 173 A : 1 3g6e : 11 CL : 400 500 : 228 H : 153 Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA Max. :178.00 Max. :104.00 Max. :104.00 Max. :800.0 Max. :800.00 Max. :81.000 Max. :15.00 Max. :55.000 Max. :9.0000 Max. :128.00 Max. :866.0 Max. :81.00 Max. :20.000 Max. :55.000 Max. :9.0000 Max. :320.010 Max. :1467.68 Max. :3.0000 Max. :531191023 Max. :6.478e+16 Max. :2.263e+24 Max. :1.007e+20 Max. :2.8594 Max. :0.8164 Max. :0.0560 Max. :7.4135 Max. :2.280e+12 Max. :9.980e+23 Max. :3.152e+24 Max. :5.832e+19 Max. :3.005e+19 Max. :5.869e+20 Max. :14.2814 Max. :44.5807 Max. :179.6831 Max. :4.1117 Max. :3.5597 Max. :32.0411 Max. :0.0778 Max. :0.0014 Max. :0.0056 Max. :0.0014 Max. :8e-04 Max. :0 Max. :183460 Max. :0.9517 Max. :0.9949 Max. :1.0000 Max. :57.618 Max. :23.513 Max. :19.245 Max. :103498612 Max. :2.493e+15 Max. :1.713e+22 Max. :4.246e+18 Max. :6.2210 Max. :3.2789 Max. :0.4408 Max. :56.1583 Max. :5.444e+11 Max. 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:2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA NA NA NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA NA NA NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA NA NA NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA NA NA NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA NA NA NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA NA NA NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA NA NA NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA’s :77 NA’s :77 NA NA NA NA NA’s :77 NA’s :77 NA NA NA NA NA’s :77 NA’s :77 NA NA NA NA NA’s :77 NA’s :77 NA NA NA NA NA’s :139 NA’s :139 NA’s :139 NA’s :139 NA’s :139 NA’s :77 NA’s :77 NA NA NA

** Wnioski z analizy podsumowanych danych: **

  • pewne wiersze zawierają warości niedostępne (NA),
  • niektóre kolumny zawierają stałe wartości,
  • różne typy zmiennych,
  • niektóre kolumny niosą redundantne informacje,
  • pewne zmienne są obliczane na podstawie całego pliku PDB, a nie tylko na podstawie ligandu,
  • niektóre kolumny niosą tą samą informację, różniąc się jedynie progiem odcięcia

Dalsze czyszczenie danych

Zamiana wartości NA na 0

dane <- dane %>% 
  replace(is.na(.), 0)

Przefiltrowanie istotności zmiennych

Po ogólnej analizie danych, można zauważyć, że pewne kolumny zawierają stałe wartości:

  • wartość 0 przyjmują: local_BAa, local_NPa, local_Ra, local_RGa, local_SRGa, local_CCSa, local_CCPa, local_ZOa, ocal_ZDa, local_ZD_minus_a, local_ZD_plus_a

  • Wartość ‘DELFWT’ przyjmuje: fo_col

  • Wartość ‘PHDELWT’ przyjmuje: fc_col

  • Wartość 0 przyjmuje: weight_col

  • Wartość 0.2 przyjmuje: grid_space

  • Wartość 1.9 przyjmuje: solvent_radius

  • Wartość 1.4 przyjmuje: solvent_opening_radius

  • Wartość 2 przyjmuje: resolution_max_limit

  • Wartość 2.5 przyjmuje: part_step_FoFc_std_min

  • Wartość 7.1 przyjmuje: part_step_FoFc_std_max

  • Wartość 0.5 przyjmuje: part_step_FoFc_std_step

Dlatego do dalszej analizy danych, nie bedziemy wykorzystywać wymienionych kolumn

dane <- dane %>%
  select(-(local_BAa:local_ZD_plus_a), -(fo_col:resolution_max_limit), -(part_step_FoFc_std_min:part_step_FoFc_std_step))

W zbiorze danych znajdują się kolumny, których wartości są obliczane na podstawie całego pliku PDB, a nie tylko na podstawie ligandu:

  • TwoFoFc_mean, TwoFoFc_std, TwoFoFc_square_std, TwoFoFc_min, TwoFoFc_max

  • Fo_mean, Fo_std, Fo_square_std, Fo_min, Fo_max

  • FoFc_mean, FoFc_std, FoFc_square_std, FoFc_min, FoFc_max

  • Fc_mean, Fc_std, Fc_square_std, Fc_min, Fc_max

  • resolution

  • TwoFoFc_bulk_mean, TwoFoFc_bulk_std, TwoFoFc_void_mean, TwoFoFc_void_std, TwoFoFc_modeled_mean, TwoFoFc_modeled_std

  • Fo_bulk_mean, Fo_bulk_std, Fo_void_mean, Fo_void_std, Fo_modeled_mean, Fo_modeled_std

  • Fc_bulk_mean, Fc_bulk_std, Fc_void_mean, Fc_void_std, Fc_modeled_mean, Fc_modeled_std

  • FoFc_bulk_mean, FoFc_bulk_std, FoFc_void_mean, FoFc_void_std, FoFc_modeled_mean, FoFc_modeled_std

  • TwoFoFc_void_fit_binormal_mean1, TwoFoFc_void_fit_binormal_std1, TwoFoFc_void_fit_binormal_mean2, TwoFoFc_void_fit_binormal_std2, TwoFoFc_void_fit_binormal_scale, TwoFoFc_solvent_fit_normal_mean, TwoFoFc_solvent_fit_normal_std

Do dalszej analizy, nie bedziemy wykorzystywać kolumn wymienionych wyżej

dane <- dane %>%
  select(-(TwoFoFc_mean:Fc_max), -resolution, -(TwoFoFc_bulk_mean:TwoFoFc_solvent_fit_normal_std))

7. Sekcje sprawdzającą korelacje między zmiennymi; sekcja ta powinna zawierać jakąś formę graficznej prezentacji korelacji

Poniższe korelogramy reprezentują korelację między poszczególnymi zmiennymi. Zbiór wszystkich kolumn, dla analizowanych danych, jest bardzo duży, dlatego po wnikliwej analizie, można było zauważyć, że kolumny part_XX niosą tą samą informację, jednak dla innego progu odcięcia. Dlatego, aby ograniczyć zbiór porównywanych zmiennych, kolumny podzielone zostały na zbiór zmiennych ogólnych (nie zawierających kolumn part), a także na rozłączne zbiory part. Korelacja nie była liczona dla dwóch różnych zbiorów part (np. między part01 i part02), a jedynie wewnątrz poszczególnych zbiorów part i atrybutów ogólnych.

Przygotowanie danych

Filtrowanie zbioru danych i eliminacja kolumn zawierających wartości stałe i tekstowe ***

# Dane bez stałych wartości i wartości tekstowych
dane_obliczenia <- dane %>%
  select(-(title:chain_id), -local_parts, -local_min, -part_00_blob_parts)

Korelogram zmiennych ogólnych

Są to wszystkie zmienne ogólne (nie zawierają kolumn part_XX_) ***

corrgram(dane_obliczenia %>%
            select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla podstawowych atrybutów", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_00

Są to wszystkie zmienne ogólne i kolumny z part_00 ***

# Dane dla part_00
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_00_blob_electron_sum:part_00_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_00", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_01

Są to wszystkie zmienne ogólne i kolumny z part_01 ***

# Dane dla part_01
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_01_blob_electron_sum:part_01_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_01", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_02

Są to wszystkie zmienne ogólne i kolumny z part_02 ***

# Dane dla part_02
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_02_blob_electron_sum:part_02_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_02", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_03

Są to wszystkie zmienne ogólne i kolumny z part_03 ***

# Dane dla part_03
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_03_blob_electron_sum:part_03_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_03", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_04

Są to wszystkie zmienne ogólne i kolumny z part_04 ***

# Dane dla part_04
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_04_blob_electron_sum:part_04_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_04", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_05

Są to wszystkie zmienne ogólne i kolumny z part_05 ***

# Dane dla part_05
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_05_blob_electron_sum:part_05_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_05", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_06

Są to wszystkie zmienne ogólne i kolumny z part_06 ***

# Dane dla part_06
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_06_blob_electron_sum:part_06_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_06", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_07

Są to wszystkie zmienne ogólne i kolumny z part_07 ***

# Dane dla part_07
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_07_blob_electron_sum:part_07_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_07", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_08

Są to wszystkie zmienne ogólne i kolumny z part_08 ***

# Dane dla part_08
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_08_blob_electron_sum:part_08_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_08", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_09

Są to wszystkie zmienne ogólne i kolumny z part_09 ***

# Dane dla part_09
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_09_blob_electron_sum:part_09_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_09", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

8. Określenie ile przykładów ma każda z klas res_name

podsumowanie <- dane %>%
  group_by(res_name) %>%
  summarise(n=n()) #Podliczenie

Liczba wszystkich klas res_name wynosi: ** 2685 **

Liczba ta jest bardzo duża, dlatego na poniższym wykresie przedstawione zostało 20 najliczniejszych klas.

Wykres 20 najliczniejszych klasy

podsumowanie <- podsumowanie %>%
  arrange(desc(n)) %>%
  slice(1:20)

podsumowanie$res_name <- factor(podsumowanie$res_name, levels=unique(podsumowanie$res_name))

ggplot(podsumowanie , aes(x = res_name, y = n, order = desc(n))) + 
  geom_bar(stat="identity") + 
  theme_bw()

9. Wykresy rozkładów liczby atomów (local_res_atom_non_h_count) i elektronów (local_res_atom_non_h_electron_sum)

rozk <- stack(dane %>% select(local_res_atom_non_h_count, local_res_atom_non_h_electron_sum))

ggplot(rozk, aes(x = values)) + geom_density(aes(group=ind, colour=ind, fill=ind), alpha=0.3)

Analizując powyższy wykres, można zauważyć, że:

10. Próbę odtworzenia następującego wykresu (oś X - liczba elektronów, oś y - liczba atomów)

d <- dane %>%
  mutate(y = 6+(round(jitter(local_res_atom_non_h_count, factor = 2.5, amount = 0))), x = 50+(round(jitter(local_res_atom_non_h_electron_sum, factor = 3.5, amount = 0)))) %>% select(x, y)

rozklad <- ggplot(d, 
  aes(x = x, 
      y = y)) +
  stat_density2d(geom="tile", aes(fill = ..density..), contour = FALSE) + #, position = position_jitter()) + 
  scale_fill_gradientn(colours=rev(brewer.pal(11, "Spectral"))) +
  coord_cartesian(xlim = c(0,650), 
                  ylim = c(0,100)) +
  scale_x_continuous(breaks = seq(0,600,100)) +
  scale_y_continuous(breaks = seq(0,100,20)) +
  theme(legend.position="none", 
        axis.title.x = element_blank(), 
        axis.title.y = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = rgb(0.368, 0.309, 0.635))) 

#Wykresy margin
ggExtra::ggMarginal(
  rozklad,
  type = 'histogram',
  margins = 'both',
  size = 5,
  xparams = list(binwidth = 6, colour = "black", fill = "red"),
  yparams = list(binwidth = 1, colour = "black", fill = "red")
)

Do stworzenia wykresu, wykozystana została biblioteka ggplot2 i ggEkstra. Druga wymieniona biblioteka umożliwiła stowrzenie wykresów na poszczególnych osiach. Do pokolorowania rozkładu wykorzystana została biblioteka RColorBrewer, z której skorzystano z typu kolorów Spectral. Należało go jednak odwrócić. Aby dokładniej przeanalizowac dane, które były skupione blisko wartości zerowych, wykorzystany został jitter.

11. Tabelę pokazującą 10 klas z największą niezgodnością liczby atomów (local_res_atom_non_h_count vs dict_atom_non_h_count) i tabelę pokazującą 10 klas z największą niezgodnością liczby elektronów (local_res_atom_non_h_electron_sum vs dict_atom_non_h_electron_sum;)

Niezgodność liczby atomów

kable(dane %>%
  mutate(blad = abs(dict_atom_non_h_count - local_res_atom_non_h_count)) %>%
  select(res_name, blad) %>%
  group_by(res_name) %>%
  summarise(minimum = min(blad),
            maximum = max(blad),
            warjancja = var(blad),
            odch_stand = sd(blad)) %>%
  arrange(desc(maximum)) %>%
  slice(1:10))
res_name minimum maximum warjancja odch_stand
PEU 64 69 12.5000 3.535534
CDL 0 60 527.6944 22.971601
PC1 15 43 252.3333 15.885003
3TL 0 33 363.0000 19.052559
CPQ 33 33 NA NaN
JEF 33 33 NA NaN
PE3 0 33 544.5000 23.334524
PEE 2 33 267.3333 16.350331
VV7 33 33 NA NaN
LI1 27 32 12.5000 3.535534

Niezgodność liczby elektronów

kable(dane %>%
  mutate(blad = abs(dict_atom_non_h_electron_sum - local_res_atom_non_h_electron_sum)) %>%
  select(res_name, blad) %>%
  group_by(res_name) %>%
  summarise(minimum = min(blad),
            maximum = max(blad),
            warjancja = var(blad),
            odch_stand = sd(blad)) %>%
  arrange(desc(maximum)) %>%
  slice(1:10))
res_name minimum maximum warjancja odch_stand
PEU 426 460 578.00 24.04163
CDL 0 360 19013.11 137.88804
PC1 91 284 11064.33 105.18713
ATG 252 252 0.00 0.00000
SAP 252 252 NA NaN
ABG 242 242 NA NaN
CPQ 225 225 NA NaN
PEE 12 224 12761.33 112.96607
VV7 224 224 NA NaN
PE3 0 222 24642.00 156.97771

12. Sekcję pokazującą rozkład wartości wszystkich kolumn zaczynających się od part_01 z zaznaczeniem (graficznym i liczbowym) średniej wartości.

part <- dane %>% select(part_01_blob_electron_sum:part_01_density_sqrt_E3)

for(i in seq_along(part)) {
  
  srednia <- mean(part[,i], rm.na=TRUE)
  min <- min(part[,i], rm.na=TRUE)
  max <- max(part[,i], rm.na=TRUE)
  
  w <- ggplot(data = part, aes(x=part[,i], label = srednia)) + 
    scale_x_continuous(colnames(part %>% select(i))) +
    scale_y_continuous("Liczność") +
    geom_histogram(aes(y = ..density.., fill = ..count..)) + geom_density(colour = "red", size = 1.5) +
    scale_fill_gradientn(colours=brewer.pal(11, "Spectral")) +
    geom_vline(data = NULL, aes(xintercept=srednia), linetype = "dashed", size=2) +
    ggtitle(paste("Rozkład wartości dla atrybutu ", colnames(part %>% select(i)), " średnia=", srednia, sep = "")) 
  
  print(w)
}

Powyższe wykresy pokazują rozkład poszczególnych zmiennych dla Part_01. Patrząc na wcześniejsze podsumowanie surowych danych, można zauważyć, że kolumny te posiadały bardzo dużą liczbę wartości pustych, które w dalszej części zostały także zamienione na wartość 0 (stąd też widoczne wysokie rozkłady wartości w okolicach 0)

13. Sekcję sprawdzającą czy na podstawie wartości innych kolumn można przewidzieć liczbę elektronów i atomów oraz z jaką dokładnością można dokonać takiej predykcji; trafność regresji powinna zostać oszacowana na podstawie miar R^2 i RMSE;

Przewidywanie liczby atomów

Do przewidywania liczby atomów wykorzystany został mogel regresji liniowej. Dodatkowo, porównany został sposób uczenia modelu regresji z wykorzystaniem standardowego próbkowania i walidacji krzyżowej. Analizując dalsze wyniki, oba podejścia nie zmieniły uzyskanych wyników. Do analizy atrybutów wykorzystane zostały wszystkie zmienna oprócz zmiennych słownikowych (dist_)

Budowanie modelu

proba <- dane %>% select(-(title:chain_id), -(dict_atom_non_h_count:dict_atom_S_count))

inTraining <- createDataPartition(y=proba$local_res_atom_non_h_count, 
                                  p= .75, 
                                  list = FALSE)

training <- proba[ inTraining,]
testing <- proba[ -inTraining,]
Dla podstawowego modelu "lm"
lmFit <- train(local_res_atom_non_h_count ~ . , 
               method="lm", 
               data = training)
Dla modelu "lm" z Cross-Validation
ctrl <- trainControl(method = "cv", number = 10)

lmCVFit <- train(local_res_atom_non_h_count ~ . , 
                 method="lm", 
                 data = training, 
                 trControl = ctrl)

Lista najbardziej wartościowych atrybutów

Dla podstawowego modelu "lm"
plot(varImp(lmFit), top = 20)

Dla modelu "lm" z Cross-Validation
plot(varImp(lmCVFit), top = 20)

Trafność regresji

Miara RMSE i R^2 dla podstawowego modelu "lm"
predVal<-predict(lmFit, testing)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
modelvalues<-data.frame(obs = testing$local_res_atom_non_h_count, pred=predVal)

defaultSummary(modelvalues)
##      RMSE  Rsquared 
## 1.4232369 0.9904637
Miara RMSE i R^2dla modelu "lm" z Cross-Validation
predVal<-predict(lmCVFit, testing)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
modelvalues<-data.frame(obs = testing$local_res_atom_non_h_count, pred=predVal)

defaultSummary(modelvalues)
##      RMSE  Rsquared 
## 1.4232369 0.9904637

Wykres predykcji wartości

Dla podstawowego modelu "lm"
predictedValues <- predict(lmFit)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
plot(training$local_res_atom_non_h_count, predictedValues)

Dla modelu "lm" z Cross-Validation
predictedValues <- predict(lmCVFit)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
plot(training$local_res_atom_non_h_count, predictedValues)

Przewidywanie liczby elektronów

Budowanie modelu

proba <- dane %>% select(-(title:chain_id), -(dict_atom_non_h_count:dict_atom_S_count))

inTraining <- createDataPartition(y=proba$local_res_atom_non_h_electron_sum, 
                                  p= .75, 
                                  list = FALSE)

training <- proba[ inTraining,]
testing <- proba[ -inTraining,]
Dla podstawowego modelu "lm"
lmFit <- train(local_res_atom_non_h_electron_sum ~ . , 
               method="lm", 
               data = training)
Dla modelu "lm" z Cross-Validation
ctrl <- trainControl(method = "cv", number = 10)

lmCVFit <- train(local_res_atom_non_h_electron_sum ~ . , 
                 method="lm", 
                 data = training, 
                 trControl = ctrl)

Lista najbardziej wartościowych atrybutów

Dla podstawowego modelu "lm"
plot(varImp(lmFit), top = 20)

Dla modelu "lm" z Cross-Validation
plot(varImp(lmCVFit), top = 20)

Trafność regresji

Miara RMSE i R^2 dla podstawowego modelu "lm"
predVal<-predict(lmFit, testing)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
modelvalues<-data.frame(obs = testing$local_res_atom_non_h_electron_sum, pred=predVal)

defaultSummary(modelvalues)
##        RMSE    Rsquared 
## 120.1233936   0.4140984
Miara RMSE i R^2dla modelu "lm" z Cross-Validation
predVal<-predict(lmCVFit, testing)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
modelvalues<-data.frame(obs = testing$local_res_atom_non_h_electron_sum, pred=predVal)

defaultSummary(modelvalues)
##        RMSE    Rsquared 
## 120.1233936   0.4140984

Wykres predykcji wartości

Dla podstawowego modelu "lm"
predictedValues <- predict(lmFit)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
plot(training$local_res_atom_non_h_electron_sum, predictedValues)

Dla modelu "lm" z Cross-Validation
predictedValues <- predict(lmCVFit)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
plot(training$local_res_atom_non_h_electron_sum, predictedValues)

14. Sekcję próbującą stworzyć klasyfikator przewidujący wartość atrybutu res_name (w tej sekcji należy wykorzystać wiedzę z pozostałych punktów oraz wykonać dodatkowe czynności, które mogą poprawić trafność klasyfikacji); trafność klasyfikacji powinna zostać oszacowana na danych inne niż uczące za pomocą mechanizmu (stratyfikowanej!) oceny krzyżowej lub (stratyfikowanego!) zbioru testowego.

Do stworzenia klasyfikatora dla zmiennej res_name, wykorzystano wiedzę uzyskaną z powyższych badań. Znając dużą liczbę różnych wartości dla zmiennej res_name, klasyfikator został wykorzystany do zbudowania modelu dla wartości zmiennych przekraczających liczbę 50. Tym samym ograniczyło to liczbę analizowanego zbioru. Kolejnym wnioskiem wywinioskowanym z eksploracji danych było dostrzeżenie korelacji między zmiennymi. Powyższe korelogramy pokazały, że niektóre kolumny nie są ze sobą powiązane i tym samym niepotrzebne jest porównywanie ich do stworzenia modelu. Dlatego ustawiony został próg korelacji 0.90. Do budowy klasyfikacji wykorzystane zostały dwa modele - jeden z nich opierał się na algorytmie GBM, a drugi na Random Forest. Analizując poszczególne wyniki, algorytm Random Forest stworzył model, który okazał się bardziej trafnym (wyższy wskażnik Kappa i Accuracy). Analizując także Confussion Matrix, można dostrzec, że klasyfikator popełniał wysoki błąd dla niektórych ligardów.

Przygotowanie danych do klasyfikacji

da <- dane %>%
  group_by(res_name) %>%
  summarise(liczba = n()) %>%
  filter(liczba > 50) 

#res_name <- dane %>%
#  filter(res_name %in% da$res_name) %>%
#  select(res_name) 

descr <- dane %>%
  filter(res_name %in% da$res_name) %>%
  select(-(res_id:dict_atom_S_count), -local_min, -title, -pdb_code) 

cols = 2:length(descr)
descr[,cols] = apply(descr[,cols], 2, function(x) as.numeric(x))

#res_name <- res_name$res_name

predictors <- names(descr)[names(descr) != "res_name"]

inTrain <- createDataPartition(descr$res_name, p = 3/4, list = FALSE)

training <- descr[inTrain,]
testing <- descr[-inTrain,]

Filtrowanie zmiennych

res_name_training <- training %>% select(res_name)
res_name_testing<- testing %>% select(res_name)

training <- training %>% select(-res_name)
testing <- testing %>% select(-res_name)

ncol(training)
## [1] 701
descrCorr <- cor(training)

highCorr <- findCorrelation(descrCorr, 0.90)


training <- training[, -highCorr]
testing <- testing[, -highCorr]

ncol(training)
## [1] 159
res_name_training <- res_name_training %>% mutate(name = paste("name_", res_name, sep="")) %>% select(name)
res_name_testing <- res_name_testing %>% mutate(name = paste("name_", res_name, sep="")) %>% select(name)

Pre-Processing

xTrans <- preProcess(training)
training <- predict(xTrans, training)
testing <- predict(xTrans, testing)
training <- data.frame(res_name_training, training)
testing <- data.frame(res_name_testing, testing)

Zmienne kontrolne

gbmGrid <- expand.grid(interaction.depth = c(1, 5, 10), 
                       n.trees = (5:10)*2, 
                       shrinkage = 0.1,
                       n.minobsinnode = 20)


ctrl <- trainControl(method = "repeatedcv", 
                     number = 2,
                     repeats = 5)

rfGrid <- expand.grid(mtry = c(10:15))

Uczenie klasyfikatora

Dla algorytmu GBM

gbmFit <- train(name~., 
                data = training,
                method = "gbm", 
                trControl = ctrl, 
                verbose = FALSE,
                bag.fraction = 0.5, 
                tuneGrid = gbmGrid)

Dla algorytmu Random Forest

rfFit <- train(name~., 
                data = training,
                method = "rf", 
                trControl = ctrl,  
                tuneGrid = rfGrid,
                ntree = 30)

Wykresy podsumowania

Dla algorytmu GBM

plot(gbmFit)

plot(gbmFit, metric = "Kappa")

plot(gbmFit, plotType = "level")

resampleHist(gbmFit)

Dla algorytmu Random Forest

plot(rfFit)

plot(rfFit, metric = "Kappa")

resampleHist(rfFit)

Podsumowanie predykcji modeli

Dla algorytmu GBM

gbmPred <- predict(gbmFit, testing)
matrix <- as.matrix(confusionMatrix(gbmPred, testing$name))

kable(matrix, digits = 3, caption = "Confusion Matrix dla GBM")
Confusion Matrix dla GBM
name_ACT name_ADP name_ATP name_BME name_CA name_CD name_CIT name_CL name_CO name_CU name_EDO name_EPE name_FAD name_FE name_FE2 name_FMN name_GDP name_GOL name_HEM name_K name_MAN name_MES name_MG name_MN name_MPD name_NA name_NAD name_NAG name_NAP name_NDP name_NI name_PEG name_PG4 name_PLP name_PO4 name_SAH name_SEP name_SO4 name_TRS name_ZN
name_ACT 14 0 0 0 0 0 0 3 0 0 2 2 0 0 0 0 0 6 0 0 0 0 3 0 0 1 0 1 0 0 0 1 0 0 2 0 0 6 0 2
name_ADP 0 12 8 0 0 0 0 0 0 0 0 0 1 0 0 2 6 0 0 0 0 0 0 2 0 0 4 0 0 2 1 1 0 0 0 2 0 1 0 0
name_ATP 0 5 8 0 2 0 0 0 0 0 0 0 0 1 0 0 4 0 0 0 0 0 1 2 0 0 1 0 0 0 0 0 0 0 0 2 0 0 1 0
name_BME 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_CA 0 1 4 2 82 0 0 4 0 4 0 1 0 6 2 0 0 8 1 3 1 1 11 8 0 3 1 0 3 0 1 0 0 0 6 0 1 11 1 15
name_CD 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1
name_CIT 0 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
name_CL 6 0 0 0 4 1 0 52 0 0 3 2 0 0 0 0 0 8 0 11 0 0 4 0 0 6 0 0 0 0 1 1 0 0 5 0 0 15 0 3
name_CO 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
name_CU 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2
name_EDO 0 0 0 3 4 0 0 1 0 0 10 0 0 0 0 0 0 14 0 1 0 0 2 1 1 2 0 0 0 0 0 1 1 1 1 0 0 5 0 0
name_EPE 0 0 0 0 0 0 0 0 0 0 1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0
name_FAD 0 2 0 0 0 0 0 0 0 0 0 0 25 0 0 0 1 0 0 0 0 0 0 1 0 0 1 3 4 1 0 0 0 0 0 0 0 0 0 0
name_FE 0 0 0 0 1 0 0 0 1 0 0 0 1 2 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 7
name_FE2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
name_FMN 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 10 0 0 1 0 0 0 0 0 0 0 0 2 1 0 1 0 1 0 0 2 0 0 0 1
name_GDP 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_GOL 9 3 0 4 7 0 5 6 1 0 38 2 1 1 0 0 0 119 1 1 5 3 12 0 10 16 1 13 0 1 1 11 3 0 4 0 1 11 6 3
name_HEM 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 62 0 0 0 2 0 0 0 2 1 0 0 0 0 0 1 0 1 0 0 0 3
name_K 0 0 0 0 1 0 0 5 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0
name_MAN 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 2 0 0 1 0 0 2 0 1
name_MES 0 1 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 2 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0
name_MG 0 0 2 1 17 1 1 3 0 0 4 1 1 0 1 0 1 7 0 2 4 0 32 4 0 8 0 6 0 0 0 0 0 0 2 0 3 8 2 10
name_MN 0 0 0 0 2 0 0 0 1 1 0 0 0 2 2 1 0 0 0 0 0 0 2 3 0 0 0 0 1 1 2 0 0 0 0 0 0 0 0 8
name_MPD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_NA 3 0 0 0 5 0 0 5 0 0 4 0 0 1 0 0 0 4 0 3 0 0 2 0 0 4 0 2 0 1 0 1 0 0 1 0 0 1 0 0
name_NAD 0 2 0 1 0 0 0 0 0 1 0 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 6 0 2 1 0 0 0 0 0 0 0 0 0 0
name_NAG 0 2 1 1 0 0 2 0 0 0 0 0 1 0 0 2 0 8 1 0 4 1 7 0 0 1 1 62 2 1 0 0 5 0 1 1 1 2 2 0
name_NAP 0 2 0 0 0 0 0 0 0 0 0 0 4 0 0 3 1 0 0 0 0 0 0 1 0 0 1 3 7 6 0 0 0 0 0 0 0 0 0 0
name_NDP 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0
name_NI 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_PEG 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 2 0 0 0 1 0 0 0 1 0 1 0 0 0 3 1 0 0 0 0 0 0 0
name_PG4 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 1 0 0 0 1 3 0 0 0 0 0 0 0
name_PLP 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0
name_PO4 0 0 0 0 1 0 0 1 1 4 1 0 0 0 0 0 0 1 0 1 0 1 2 3 1 1 0 0 0 0 0 0 0 0 8 0 0 13 0 5
name_SAH 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0
name_SEP 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 5 0 0 0
name_SO4 6 2 0 0 24 2 1 19 2 1 4 3 0 3 0 0 0 11 0 4 0 6 14 1 0 9 0 0 0 1 3 0 1 0 37 0 1 212 1 16
name_TRS 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0
name_ZN 1 0 1 0 8 8 0 0 7 14 0 0 0 11 7 0 0 0 3 0 0 0 2 16 0 0 0 0 0 0 6 0 0 1 7 0 1 7 0 134

Dla algorytmu Random Forest

rfPred <- predict(rfFit, testing)
matrix <- as.matrix(confusionMatrix(rfPred, testing$name))

kable(matrix, digits = 3, caption = "Confusion Matrix dla Random Forest")
Confusion Matrix dla Random Forest
name_ACT name_ADP name_ATP name_BME name_CA name_CD name_CIT name_CL name_CO name_CU name_EDO name_EPE name_FAD name_FE name_FE2 name_FMN name_GDP name_GOL name_HEM name_K name_MAN name_MES name_MG name_MN name_MPD name_NA name_NAD name_NAG name_NAP name_NDP name_NI name_PEG name_PG4 name_PLP name_PO4 name_SAH name_SEP name_SO4 name_TRS name_ZN
name_ACT 9 0 0 0 0 0 0 2 0 0 1 1 0 0 0 0 0 3 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 4 0 0
name_ADP 0 11 7 0 0 0 0 0 0 0 0 0 1 1 0 1 9 0 0 0 0 0 1 2 0 0 4 0 0 3 0 0 0 0 0 1 0 0 0 0
name_ATP 0 7 8 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 4 0 0 0 0
name_BME 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_CA 1 1 2 1 80 1 1 3 0 5 0 1 0 7 2 0 0 2 1 1 0 0 7 8 0 3 0 0 1 0 1 0 0 0 4 0 1 7 0 16
name_CD 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
name_CIT 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1
name_CL 7 0 0 0 6 1 0 47 1 0 3 0 0 1 0 0 0 6 0 12 2 0 3 0 0 7 0 0 0 0 0 1 0 0 3 0 0 14 0 2
name_CO 0 0 0 0 0 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_CU 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
name_EDO 3 0 0 1 1 0 0 0 0 0 12 0 0 0 0 0 0 15 0 1 1 0 3 1 0 1 0 0 0 0 0 3 0 0 1 0 0 5 0 0
name_EPE 0 0 0 0 1 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0
name_FAD 0 2 0 0 0 0 0 0 0 1 0 0 31 0 0 0 2 0 1 0 0 0 1 0 0 0 1 1 5 1 0 0 0 0 0 0 0 0 0 1
name_FE 1 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4
name_FE2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_FMN 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 9 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
name_GDP 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_GOL 8 3 0 11 11 0 6 16 1 0 45 4 2 1 0 0 0 136 0 6 4 4 22 0 11 21 0 11 0 0 2 13 8 0 7 0 2 15 7 8
name_HEM 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 65 0 0 0 0 1 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 2
name_K 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_MAN 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_MES 0 0 0 0 2 0 0 1 0 0 0 3 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
name_MG 0 2 2 0 11 1 2 5 0 0 1 1 0 0 0 0 0 6 0 1 2 0 32 3 1 6 1 2 1 1 0 0 0 0 2 0 1 9 4 4
name_MN 0 1 0 0 3 1 0 0 2 0 0 0 0 0 1 0 0 0 1 0 0 0 3 5 0 2 0 0 1 0 1 0 0 0 1 0 0 0 0 2
name_MPD 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
name_NA 3 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 2 0 0 0 0 1 0 0 5 0 0 0 1 0 0 0 0 0 0 0 1 0 1
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